Descriptive statistics about the economic literature on quality
1- Most quoted items by decades
1.1- 1980-1989
setwd("C:/Users/jgrad/Desktop/Network_Quality/Network_Quality")
load("Data_7_finalbase.rda")
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# Modification qualitative d'erreurs restantes constatées en explorant la base: Can be continued -------------------
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eoeoe$identifiant[eoeoe$identifiant=="LANCASTER (1966) 74 65) 132"]<-"LANCASTER (1966) 74 132"
eoeoe$identifiant[eoeoe$identifiant=="LANCASTER (1966) AB 74"]<-"LANCASTER (1966) 74 132"
eoeoe$identifiant[eoeoe$identifiant=="LANCASTER (1971) AB 40"]<-"LANCASTER (1971) AB AB"
eoeoe$identifiant[eoeoe$identifiant=="LANCASTER (1972) AB AB"]<-"LANCASTER (1971) AB AB"
eoeoe$identifiant[eoeoe$identifiant=="LANCASTER , 1971. AB AB"]<-"LANCASTER (1971) AB AB"
eoeoe$identifiant[eoeoe$identifiant=="LANCASTER (1980) 116 281"]<-"LANCASTER (1979) AB AB"
eoeoe$identifiant[eoeoe$identifiant=="AKERLOF (1970) AB 89"]<-"AKERLOF (1970) 84 488"
eoeoe$identifiant[eoeoe$identifiant=="AKERLOF (1970) AB AB"]<-"AKERLOF (1970) 84 488"
eoeoe$identifiant[eoeoe$identifiant=="MUSSA (1978) AB AB"]<-"MUSSA (1978) 18 301"
eoeoe$identifiant[eoeoe$identifiant=="BAGWELL (1985) AB AB"]<-"BAGWELL (1988) 19 59"
eoeoe$identifiant[eoeoe$identifiant=="BAGWELL (2007) AB AB"]<-"BAGWELL (2007) 3 1701"
eoeoe$identifiant[eoeoe$identifiant=="BAGWELL (1988) AB AB"]<-"BAGWELL (1991) 81 224"
eoeoe$identifiant[eoeoe$identifiant=="BAGWELL (1991) 81 . AB"]<-"BAGWELL (1991) 81 224"
eoeoe$identifiant[eoeoe$identifiant=="BAGWELL 1991 81 AB"]<-"BAGWELL (1991) 81 224"
eoeoe$identifiant[eoeoe$identifiant=="TIROLE (1990) AB AB"]<-"TIROLE (1988) AB AB"
eoeoe$identifiant[eoeoe$identifiant=="TIROLE (1994) AB AB"]<-"TIROLE (1988) AB AB"
eoeoe$identifiant[eoeoe$identifiant=="TIROLE (1998) AB AB"]<-"TIROLE (1988) AB AB"
eoeoe$identifiant[eoeoe$identifiant=="SPENCE (1973) AB AB"]<-"SPENCE (1973) 87 355"
# On calcule la fréquence de chaque identifiant
freq<-table(eoeoe$identifiant)
freq<-as.data.frame(freq)
# On stocke la fréquence comme nouvelle variable, ce qui nous servira à renseigner la taille des noeuds dans les graphes
eoeoe$frequence<-0
for(i in 1:nrow(eoeoe)){
j<-which(eoeoe$identifiant[i] == freq$Var1)
eoeoe$frequence[i]<-freq$Freq[j]
}
load("./Data_5_nodes.rda")
desco$Datepubli<-as.character(desco$Datepubli)
eoeoe$anneecitant<-0
for(i in 1:nrow(eoeoe)){
j<-which(eoeoe$V2[i] == desco$Cartel)
eoeoe$anneecitant[i] <- desco$Datepubli[j]
}
library(readr)
library(stringr)
eoeoe$anneecitant<-str_replace_all(string = eoeoe$anneecitant, pattern = "\\(", replacement = "")
eoeoe$anneecitant<-str_replace_all(string = eoeoe$anneecitant, pattern = "\\)", replacement = "")
eoeoe$anneecitant<-as.numeric(eoeoe$anneecitant)
freq19811990<-eoeoe[eoeoe$anneecitant==1980|eoeoe$anneecitant==1981|eoeoe$anneecitant==1982|eoeoe$anneecitant==1983|eoeoe$anneecitant==1984|eoeoe$anneecitant==1985|eoeoe$anneecitant==1986|eoeoe$anneecitant==1987|eoeoe$anneecitant==1988|eoeoe$anneecitant==1989,]
freq19912000<-eoeoe[eoeoe$anneecitant==1990|eoeoe$anneecitant==1991|eoeoe$anneecitant==1992|eoeoe$anneecitant==1993|eoeoe$anneecitant==1994|eoeoe$anneecitant==1995|eoeoe$anneecitant==1996|eoeoe$anneecitant==1997|eoeoe$anneecitant==1998|eoeoe$anneecitant==1999,]
freq20012010<-eoeoe[eoeoe$anneecitant==2000|eoeoe$anneecitant==2001|eoeoe$anneecitant==2002|eoeoe$anneecitant==2003|eoeoe$anneecitant==2004|eoeoe$anneecitant==2005|eoeoe$anneecitant==2006|eoeoe$anneecitant==2007|eoeoe$anneecitant==2008|eoeoe$anneecitant==2009,]
freq20112020<-eoeoe[eoeoe$anneecitant==2010|eoeoe$anneecitant==2011|eoeoe$anneecitant==2012|eoeoe$anneecitant==2013|eoeoe$anneecitant==2014|eoeoe$anneecitant==2015|eoeoe$anneecitant==2016|eoeoe$anneecitant==2017|eoeoe$anneecitant==2018|eoeoe$anneecitant==2019,]
freq19811990<-table(freq19811990$identifiant)
freq19811990<-as.data.frame(freq19811990)
freq19912000<-table(freq19912000$identifiant)
freq19912000<-as.data.frame(freq19912000)
freq20012010<-table(freq20012010$identifiant)
freq20012010<-as.data.frame(freq20012010)
freq20112020<-table(freq20112020$identifiant)
freq20112020<-as.data.frame(freq20112020)
top19811990<-freq19811990[rev(order(freq19811990$Freq)),]
top19912000<-freq19912000[rev(order(freq19912000$Freq)),]
top20012010<-freq20012010[rev(order(freq20012010$Freq)),]
top20112020<-freq20112020[rev(order(freq20112020$Freq)),]
top19811990<-top19811990[1:nrow(top19811990),]
top19912000<-top19912000[1:nrow(top19912000),]
top20012010<-top20012010[1:nrow(top20012010),]
top20112020<-top20112020[1:nrow(top20112020),]
row.names(top19811990)<-1:nrow(top19811990)
row.names(top19912000)<-1:nrow(top19912000)
row.names(top20012010)<-1:nrow(top20012010)
row.names(top20112020)<-1:nrow(top20112020)
top19811990<-top19811990[1:25,]
top19912000<-top19912000[1:25,]
top20012010<-top20012010[1:25,]
top20112020<-top20112020[1:25,]
for(i in 1:nrow(top19811990)){
j<-which(top19811990$Var1[i] == eoeoe$identifiant)
z<-which(paste(eoeoe$Firstauthor[j],eoeoe$Datepubli[j],eoeoe$Issue[j],eoeoe$Page[j])==eoeoe$identifiant[j])[1]
top19811990$V1[i] <- eoeoe$V1[j[z]]
}
for(i in 1:nrow(top19912000)){
j<-which(top19912000$Var1[i] == eoeoe$identifiant)
z<-which(paste(eoeoe$Firstauthor[j],eoeoe$Datepubli[j],eoeoe$Issue[j],eoeoe$Page[j])==eoeoe$identifiant[j])[1]
top19912000$V1[i] <- eoeoe$V1[j[z]]
}
for(i in 1:nrow(top20012010)){
j<-which(top20012010$Var1[i] == eoeoe$identifiant)
z<-which(paste(eoeoe$Firstauthor[j],eoeoe$Datepubli[j],eoeoe$Issue[j],eoeoe$Page[j])==eoeoe$identifiant[j])[1]
top20012010$V1[i] <- eoeoe$V1[j[z]]
}
for(i in 1:nrow(top20112020)){
j<-which(top20112020$Var1[i] == eoeoe$identifiant)
z<-which(paste(eoeoe$Firstauthor[j],eoeoe$Datepubli[j],eoeoe$Issue[j],eoeoe$Page[j])==eoeoe$identifiant[j])[1]
top20112020$V1[i] <- eoeoe$V1[j[z]]
}
top19811990<-top19811990[,c(3)]
top19912000<-top19912000[,c(3)]
top20012010<-top20012010[,c(3)]
top20112020<-top20112020[,c(3)]
top19811990<-as.data.frame(top19811990)
top19912000<-as.data.frame(top19912000)
top20012010<-as.data.frame(top20012010)
top20112020<-as.data.frame(top20112020)
names(top19811990)[names(top19811990) == "top19811990"] <- "Most quoted references 1980-1989"
names(top19912000)[names(top19912000) == "top19912000"] <- "Most quoted references 1990-1999"
names(top20012010)[names(top20012010) == "top20012010"] <- "Most quoted references 2000-2009"
names(top20112020)[names(top20112020) == "top20112020"] <- "Most quoted references 2010-2019"
for(i in 1:nrow(top19811990)){
top19811990$`Most quoted references 1980-1989`[i]<-paste(i,"-",top19811990$`Most quoted references 1980-1989`[i])
}
for(i in 1:nrow(top19912000)){
top19912000$`Most quoted references 1990-1999`[i]<-paste(i,"-",top19912000$`Most quoted references 1990-1999`[i])
}
for(i in 1:nrow(top20012010)){
top20012010$`Most quoted references 2000-2009`[i]<-paste(i,"-",top20012010$`Most quoted references 2000-2009`[i])
}
for(i in 1:nrow(top20112020)){
top20112020$`Most quoted references 2010-2019`[i]<-paste(i,"-",top20112020$`Most quoted references 2010-2019`[i])
}
library(knitr)
library(kableExtra)
kable(top19811990)| Most quoted references 1980-1989 |
|---|
| 1 - Selten, R., Re-examination of the perfectness concept for equilibrium points in extensive games (1975) International J. Game Theory, 4, pp. 25-55; |
| 2 - Hotelling, H., Stability in competition (1929) The Economic Journal, 39 (153), pp. 41-57; |
| 3 - Akerlof, G.A., The market for” lemons”: Quality uncertainty and the market mechanism (1970) The quarterly journal of economics, 84 (3), pp. 488-500; |
| 4 - Salop, S.C., Monopolistic competition with outside goods (1979) Bell J. Econom., 10 (1), pp. 141-156; |
| 5 - Lancaster, K., (1979) Variety, Equity, and Efficiency., , New York: Columbia University Press; |
| 6 - Dixit, A., Stiglitz, J., Monopolistic competition and optimum product diversity (1977) American Economic Review, 67 (3), pp. 297-308; |
| 7 - Spence, A.M., Product selection, fixed costs, and monopolistic competition (1976) Review of Economic Studies, 43 (2), pp. 217-235; |
| 8 - Mussa, M., Rosen, S., Monopoly and product quality (1978) Journal of Economic theory, 18, pp. 301-317; |
| 9 - D’Aspremont, C., Gabszewicz, J., Thisse, J.F., On Hotelling’s Stability in Competition (1979) Econometrica, 47, pp. 1145-1151; |
| 10 - Shapiro, C., Premiums for high quality products as returns to reputations (1983) The Quarterly Journal of Economics, 98 (4), pp. 659-679; |
| 11 - Shaked, A., Sutton, J., Relaxing Price Competition Through Product Differentiation (1982) Review of Economic Studies, 49, pp. 3-13; |
| 12 - Prescott, E.C., Visscher, M., Sequential location among firms with foresight (1977) Bell Journal of Economics, 8, pp. 378-393; |
| 13 - Harsanyi, J., Games with incomplete information played by Bayesian players (1967) Manage. Sci., 14, pp. 159-182; |
| 14 - Varian, H., A Model of Sales (1980) A.E. R, 70, pp. 651-659; |
| 15 - Scherer, F.M., (1980) Industrial Market Structure and Economic Performance, , Second ed. Chicago: Rand McNally; |
| 16 - Rubinstein, A., Perfect equilibrium in a bargaining model (1982) Econometrica, 50, pp. 97-109; |
| 17 - Rodriguez, C.A., The Quality of Imports and the Differential Welfare Effects of Tariffs, Quotas, and Quality Controls as Protective Devices (1979) Canadian Journal of Economics, 12, pp. 439-449; |
| 18 - Milgrom, P., Roberts, J., Limit pricing and entry under incomplete information: an equilibrium analysis (1982) Econometrica, 50, pp. 443-459; |
| 19 - Grossman, S., The informational role of warranties and private disclosure about product quality (1981) J. Law Econ., 24 (3), pp. 461-483; |
| 20 - Falvey, R., “The Composition of Trade within Import-Restricted Products Categories” (1979) Journal of Political Economy, 87, pp. 1105-1114; |
| 21 - Economides, The principle of minimum differentiation revisited (1982) Discussion paper no. 134, , Columbia University, New York; |
| 22 - Chan, Y.S., Leland, H., Prices and Qualities in Markets with Costly Information," (1982) Review of Economic Studies, 49, pp. 499-516; |
| 23 - Spence, A.M., Monopoly, quality, and regulation (1975) Bell Journal of Economics, 6 (2), pp. 417-429; |
| 24 - Sobel, J., Takahashi, I., A multistage model of bargaining (1983) Review of Economic Studies, 50, pp. 411-426; |
| 25 - Shilony, Y., Mixed pricing in oligopoly (1977) Journal of Economic Theory, 14, pp. 373-388; |
1.2- 1990-1999
kable(top19912000)| Most quoted references 1990-1999 |
|---|
| 1 - Tirole, J., (1988) The theory of industrial organization, , MIT Press; |
| 2 - Akerlof, G.A., The market for” lemons”: Quality uncertainty and the market mechanism (1970) The quarterly journal of economics, 84 (3), pp. 488-500; |
| 3 - Mussa, M., Rosen, S., Monopoly and product quality (1978) Journal of Economic theory, 18, pp. 301-317; |
| 4 - Milgrom, P., Roberts, J., Price and advertising signals of product quality (1986) The Journal of Political Economy, 94 (4), pp. 796-821; |
| 5 - Klein, B., Leffler, K.B., The role of market forces in assuring contractual performance (1981) Journal of Political Economy, 89 (4), pp. 615-641; |
| 6 - Shaked, A., Sutton, J., Relaxing Price Competition Through Product Differentiation (1982) Review of Economic Studies, 49, pp. 3-13; |
| 7 - Cho, I.K., Kreps, D., Signaling games and stable equilibria (1987) Quart. J. Econom, 102 (2), pp. 179-221; |
| 8 - Farrell, J., Saloner, G., Standardization, Compatibility and Innovation (1985) The Rand Journal of Economics, 16, pp. 70-83; |
| 9 - Crosby, P.B., (1979) Quality Is Free, , McGraw-Hill New York; |
| 10 - Katz, M.L., Shapiro, C., Technology adoption in the presence of network externalities (1986) Journal of Political Economy, 94, pp. 822-841; |
| 11 - Hotelling, H., Stability in competition (1929) The Economic Journal, 39 (153), pp. 41-57; |
| 12 - Nelson, P., Advertising as information (1974) The Journal of Political Economy, 82 (4), pp. 729-754; |
| 13 - Porter, M.E., (1980) Competitive Strategy, , The Free Press, New York; |
| 14 - Nelson, P., Information and consumer behavior (1970) The Journal of Political Economy, 78 (2), pp. 311-329; |
| 15 - Kreps, D.M., Wilson, R., Sequential Equilibria (1982) Econometrica, 50, pp. 863-894; |
| 16 - Katz, M., Shapiro, C., Network externalities, competition, and compatibility (1985) American Economic Review, 75 (3), pp. 424-440; |
| 17 - Farrell, J., Saloner, G., Installed base and compatibility: Innovation, product preannouncements, and prédation (1986) American Economic Review, 76 (5), pp. 940-955; |
| 18 - Shapiro, C., Premiums for high quality products as returns to reputations (1983) The Quarterly Journal of Economics, 98 (4), pp. 659-679; |
| 19 - Parasuraman, A., Zeithaml, V.A., Berry, L.L., A conceptual model of service quality and its implications for future research (1985) Journal of Marketing, 49, pp. 41-50; |
| 20 - Deming, W.E., (1986) Out of the Crisis, , MIT Press Cambridge, MA; |
| 21 - David, P.A., Clio and the economics of QWERTY (1985) American Economic Review, 75, pp. 332-337; |
| 22 - Bagwell, K., Riordan, M.H., High and declining prices signal product quality (1991) American Economic Review, 81 (1), pp. 224-239; |
| 23 - Parasuraman, A., Berry, L.L., Zeithaml, V.A., Zeithaml, V.A., Berry, L.L., SERVQUAL: Multiple-item scale for measuring consumer perceptions of service quality (1988) Journal of Retailing, 64 (1), pp. 12-40; |
| 24 - Saraph, J.V., Benson, P.G., Schroeder, R.G., An instrument for measuring the critical factors of quality management (1989) Decis. Sci., 20 (4), pp. 810-829; |
| 25 - Oliver, R.L., A cognitive model of the antecedents and consequences of satisfaction decisions (1980) Journal of Marketing Research, 17, pp. 460-469., November; |
1.3- 2000-2009
kable(top20012010)| Most quoted references 2000-2009 |
|---|
| 1 - Akerlof, G.A., The market for” lemons”: Quality uncertainty and the market mechanism (1970) The quarterly journal of economics, 84 (3), pp. 488-500; |
| 2 - Tirole, J., (1988) The theory of industrial organization, , MIT Press; |
| 3 - Mussa, M., Rosen, S., Monopoly and product quality (1978) Journal of Economic theory, 18, pp. 301-317; |
| 4 - Powell, T.C., Total quality management as competitive advantage: A review and empirical study (1995) Strateg. Manag. J., 16 (1), pp. 15-37; |
| 5 - Flynn, B.B., Schroeder, R.G., Sakakibara, S., A framework for quality management research and an associated measurement instrument (1994) J. Oper. Manag., 11 (4), pp. 339-366; |
| 6 - Deming, W.E., (1986) Out of the Crisis, , MIT Press Cambridge, MA; |
| 7 - Samson, D., Terziovski, M., The relations between total quality management practices and operational performance (1999) Journal of Operations Management, 17 (4), pp. 393-409; |
| 8 - Terziovski, M., Samson, D., Dow, D., The business value of quality management systems certification. Evidence from Australia and New Zealand (1997) J. Oper. Manag., 15, pp. 1-18; |
| 9 - Klein, B., Leffler, K.B., The role of market forces in assuring contractual performance (1981) Journal of Political Economy, 89 (4), pp. 615-641; |
| 10 - Milgrom, P., Roberts, J., Price and advertising signals of product quality (1986) The Journal of Political Economy, 94 (4), pp. 796-821; |
| 11 - Hair, J.F., Jr., Anderson, R.E., Tatham, R.L., Black, W.C., (1998) Multivariate data analysis, , Prentice-Hall, Englewood Cliffs, NJ; |
| 12 - Ahire, S.L., Golhar, D.Y., Waller, M.A., Development and validation of TQM implementation constructs (1996) Decis. Sci., 27 (1), pp. 23-53; |
| 13 - Shapiro, C., Premiums for high quality products as returns to reputations (1983) The Quarterly Journal of Economics, 98 (4), pp. 659-679; |
| 14 - Saraph, J.V., Benson, P.G., Schroeder, R.G., An instrument for measuring the critical factors of quality management (1989) Decis. Sci., 20 (4), pp. 810-829; |
| 15 - Nunnally, J.C., (1978) Psychometric Theory, , seconded. McGraw-Hill New York; |
| 16 - Nelson, P., Information and consumer behavior (1970) The Journal of Political Economy, 78 (2), pp. 311-329; |
| 17 - Anderson, S.W., Daly, D., Johnson, M.F., Why Firms Seek ISO 9000 Certification: Regulatory Compliance or Competitive Advantage (1999) Production and Operations Management, 8 (1), pp. 28-43; |
| 18 - Parasuraman, A., Berry, L.L., Zeithaml, V.A., Zeithaml, V.A., Berry, L.L., SERVQUAL: Multiple-item scale for measuring consumer perceptions of service quality (1988) Journal of Retailing, 64 (1), pp. 12-40; |
| 19 - Darby, M., Karni, E., Free competition and the optimal amount of fraud (1973) Journal of Law and Economics, 16, pp. 67-88; |
| 20 - Berry, S., Levinsohn, J., Pakes, A., Automobile Prices in Market Equilibrium (1995) Econometrica, 63, pp. 841-890; |
| 21 - Flynn, B.B., Schroeder, R.G., Sakakibara, S., The impact of quality management practices on performance and competitive advantage (1995) Decis. Sci., 26 (5), pp. 659-691; |
| 22 - Dean, J.W., Bowen, D.E., Management theory and total quality: Improving research and practice through theory development (1994) Academy of Management Review, 19 (3), pp. 392-418; |
| 23 - Nelson, P., Advertising as information (1974) The Journal of Political Economy, 82 (4), pp. 729-754; |
| 24 - Anderson, J.C., Gerbing, D.W., Structural equation modeling in practice: A review and recommended two-step approach (1988) Psychol. Bull., 103 (3), pp. 411-423; |
| 25 - Hackman, J.R., Wageman, R., Total quality management: Empirical, conceptual, and practical issues (1995) Administrative Science Quarterly, 40 (2), pp. 309-343; |
1.4- 2010-2019
kable(top20112020)| Most quoted references 2010-2019 |
|---|
| 1 - Melitz, M., The impact of trade on intra-industry reallocations and aggregate industry productivity (2003) Econometrica, 71 (6), pp. 1695-1725; |
| 2 - Verhoogen, E., Trade, quality upgrading and wage inequality in the Mexican manufacturing sector (2008) Q. J. Econ., 123 (2), pp. 489-530 |
| 3 - Khandelwal, A., The long and short (of) quality ladders (2010) Rev. Econ. Stud., 77 (4), pp. 1450-1476; |
| 4 - Akerlof, G.A., The market for” lemons”: Quality uncertainty and the market mechanism (1970) The quarterly journal of economics, 84 (3), pp. 488-500; |
| 5 - Nelson, P., Information and consumer behavior (1970) The Journal of Political Economy, 78 (2), pp. 311-329; |
| 6 - Melitz, M., Ottaviano, G., Market size, trade, and productivity (2008) Rev. Econ. Stud., 75, pp. 295-316; |
| 7 - Schott, P.K., “Across-Product versus Within-Product Specialization in International Trade,” (2004) Quarterly Journal of Economics, 119, pp. 647-678; |
| 8 - Hallak, J.C., “Product Quality and the Direction of Trade,” (2006) Journal of International Economics, 68, pp. 238-265; |
| 9 - Bernard, A.B., Bradford Jensen, J., Redding, S.J., Schott, P.K., Firms in International Trade (2007) J. Econ. Perspectives, 21, pp. 105-130., (Summer); |
| 10 - Bernard, A.B., Eaton, J., Jensen, J.B., Kortum, S., Plants and productivity in international trade (2003) Am. Econ. Rev., 93 (4), pp. 1268-1290; |
| 11 - Hummels, D., Klenow, P., The Variety and Quality of a Nations Exports.” (2005) A.E.R., 95, pp. 704-723., (June); |
| 12 - Kugler, M., Verhoogen, E., Prices, plant size, and product quality (2012) Rev. Econ. Stud., 79 (1), pp. 307-339; |
| 13 - Olley, G.S., Pakes, A., The Dynamics of Productivity in the Telecommunications Equipment Industry (1996) Econometrica, 64, pp. 1263-1297; |
| 14 - Baldwin, R., Harrigan, J., Zeros, quality and space: trade theory and trade evidence (2011) Am. Econ. J.: Microecon., 3, pp. 60-88; |
| 15 - Corbett, C.J., Montes-Sancho, M.J., Kirsch, D.A., The financial impact of ISO 9000 certification in the United States: an empirical analysis (2005) Manag Sci, 51, pp. 1046-1059; |
| 16 - Helpman, E., Melitz, M., Rubinstein, Y., Estimating trade flows: trading partners and trading volumes (2008) The Quarterly Journal of Economics, 123 (2), pp. 441-487; |
| 17 - Broda, C., Weinstein, D.E., ‘Globalization and the Gains from Variety’ (2006) Quarterly Journal of Economics, 121, pp. 541-586; |
| 18 - King, A.A., Lenox, M.J., Terlaak, A., The strategic use of decentralized institutions: exploring certification with the ISO 14001 management standard (2005) Academy of Management Journal, 48, pp. 1091-1106; |
| 19 - Hallak, J.C., Schott, P.K., “Estimating Cross-Country Differences in Product Quality,” (2011) Quarterly Journal of Economics, 126, pp. 417-474; |
| 20 - Foster, L., Haltiwanger, J., Syverson, C., “Reallocation, Firm Turnover, and Efficiency: Selection on Productivity or Profitability?,” (2008) American Economic Review, 98, pp. 394-425; |
| 21 - Chaney, T., Distorted Gravity: The Intensive and Extensive Margins of International Trade (2008) A.E.R., 98, pp. 1707-1721., (September); |
| 22 - Tirole, J., (1988) The theory of industrial organization, , MIT Press; |
| 23 - Mussa, M., Rosen, S., Monopoly and product quality (1978) Journal of Economic theory, 18, pp. 301-317; |
| 24 - Eaton, J., Kortum, S., Technology, Geography, and Trade (2002) Econometrica, 70, pp. 1741-1779., (September); |
| 25 - Maertens, M., Swinnen, J.F.M., Trade, standards, and poverty: evidence from Senegal (2009) World Dev., 37 (1), pp. 161-178; |
1.5- Summary chart
top19811990<-freq19811990[rev(order(freq19811990$Freq)),]
top19912000<-freq19912000[rev(order(freq19912000$Freq)),]
top20012010<-freq20012010[rev(order(freq20012010$Freq)),]
top20112020<-freq20112020[rev(order(freq20112020$Freq)),]
top19811990<-top19811990[1:nrow(top19811990),]
top19912000<-top19912000[1:nrow(top19912000),]
top20012010<-top20012010[1:nrow(top20012010),]
top20112020<-top20112020[1:nrow(top20112020),]
top19811990$Freq<-1:nrow(top19811990)
top19912000$Freq<-1:nrow(top19912000)
top20012010$Freq<-1:nrow(top20012010)
top20112020$Freq<-1:nrow(top20112020)
top19811990$YEAR<-"1980-1989"
top19912000$YEAR<-"1990-1999"
top20012010$YEAR<-"2000-2009"
top20112020$YEAR<-"2010-2019"
top19811990<-top19811990[1:25,]
top19912000<-top19912000[1:25,]
top20012010<-top20012010[1:25,]
top20112020<-top20112020[1:25,]
top<-rbind(top19811990,top19912000,top20012010,top20112020)
library(ggplot2)
library(dplyr)
top$Var1<-str_replace_all(string = top$Var1, pattern = "D'ASPREMONT", replacement = "DASPREMONT")
top$Var1<-str_replace_all(string = top$Var1, pattern = " [[:digit:]].*$", replacement = "")
top$Var1<-str_replace_all(string = top$Var1, pattern = " AB.*$", replacement = "")
my_theme <- function() {
# Colors
color.background = "white"
color.text = "#22211d"
# Begin construction of chart
theme_bw(base_size=15) +
# Format background colors
theme(panel.background = element_rect(fill=color.background,
color=color.background)) +
theme(plot.background = element_rect(fill=color.background,
color=color.background)) +
theme(panel.border = element_rect(color=color.background)) +
theme(strip.background = element_rect(fill=color.background,
color=color.background)) +
# Format the grid
theme(panel.grid.major.y = element_blank()) +
theme(panel.grid.minor.y = element_blank()) +
theme(axis.ticks = element_blank()) +
# Format the legend
theme(legend.position = "none") +
# Format title and axis labels
theme(plot.title = element_text(color=color.text, size=5, face = "bold")) +
theme(axis.title.x = element_text(size=0, color="black", face = "bold")) +
theme(axis.title.y = element_text(size=0, color="black", face = "bold",
vjust=1.25)) +
theme(axis.text.x = element_text(size=0, vjust=0.5, hjust=0.5,
color = color.text)) +
### Taille des nombre
theme(axis.text.y = element_text(size=2.5, color = color.text)) +
theme(strip.text = element_text(face = "bold")) +
# Plot margins
# Contour du graphique
theme(plot.margin = unit(c(0, 0.2, 0.3, 0), "cm"))
}
library(ggiraph)
gg_point=ggplot(top,aes(x = as.factor(YEAR), y = Freq, group = Var1)) +
# Taille des lignes
geom_line_interactive(aes(color = Var1, alpha = 1,tooltip=Var1, data_id = Var1), size = 0.3) +
# Taille des points
geom_point_interactive(aes(x = as.factor(YEAR), y = Freq, group = Var1, color = Var1, alpha = 1, tooltip = Var1, data_id = Var1), size = 1) +
geom_point_interactive(color = "#FFFFFF", size = 0.01) +
scale_y_reverse(breaks = 1:table(top$YEAR)[[1]]) +
scale_x_discrete(breaks = 1:length(table(top$YEAR))) +
theme(legend.position = 'none') +
# le x designe le positionnement des auteurs à gauche
geom_text(data = top %>% filter(YEAR == "1980-1989"),
aes(label = Var1, x = 0.42) , hjust = 0.0,
fontface = "bold", color = "#888888", size = 0.7) +
# idem mais à droite
geom_text(data = top %>% filter(YEAR == "2010-2019"),
aes(label = Var1, x = 4.5) , hjust = 1.0,
fontface = "bold", color = "#888888", size = 0.7) +
labs(x = '', y = '', title = 'Most quoted references (1980-1989, 1990-1999, 2000-2009, 2010-2019)') +
my_theme()
girafe(ggobj = gg_point, width_svg = 3, height_svg = 2,
options = list(
opts_hover_inv(css = "opacity:0.1;"),
opts_hover(css = "stroke-width:2;")
))3- Bibliographic coupling 2010-2019
##### On a une colonne d'identifiant unique des articles et une colonne avec les refs biblios
##### recodées, on peut donc soumettre ça au package d'Aurelien
set.seed(12)
setwd("C:/Users/jgrad/Desktop/Network_Quality/Network_Quality")
load("Data_5_nodes.rda")
load("Data_7_finalbase.rda")
library(readr)
library(stringr)
#library(bibliometrix)
library(tidyverse)
library(biblionetwork)
#library(devtools)
#devtools::install_github("agoutsmedt/biblionetwork")
eoeoe2<-eoeoe
simple<-unique(eoeoe2$V2)
simple<-as.data.frame(simple)
#setdiff(desco$Cartel,simple[,1])
desco<-desco[desco$Cartel!=108237,]
comp<-cbind(simple,desco$Cartel)
desco$sloubi<-str_detect (string = desco$Datepubli, pattern = "2010|2011|2012|2013|2014|2015|2016|2017|2018|2019")
#desco$sloubi<-str_detect (string = desco$Datepubli, pattern = "1970|1971|1972|1973|1974|1975|1976|1977|1978|1979|1980|1981|1982|1983|1984|1985|1986|1987|1988|1989|1990|1991|1992|1993|1994|1995|1996|1997|1998|1990|2000")
desco<-desco[desco$sloubi==T,]
Cartel<-desco$Cartel
eoeoe2$toofar<-"AB"
for(i in 1:nrow(eoeoe2)){
if(eoeoe2$V2[i]%in%Cartel){eoeoe2$toofar[i]<-T}else{eoeoe2$toofar[i]<-F}}
eoeoe2<-eoeoe2[eoeoe2$toofar==T,]
Carlito<-unique(eoeoe2$V2)
Carlito<-as.data.frame(Carlito)
Cartel<-as.data.frame(Cartel)
Cabal<-cbind(Carlito,Cartel)
eoeoe2$V2<-as.character(eoeoe2$V2)
eoeoe2$identifiant<-as.character(eoeoe2$identifiant)
desco$Cartel<-as.character(desco$Cartel)
Basefinale<-biblio_coupling(eoeoe2, source = "V2", ref = "identifiant", normalized_weight_only = FALSE, weight_threshold = 2)
#devtools::install_github("agoutsmedt/networkflow")
library(biblionetwork)
library(magrittr)
library(dplyr)
library(tidygraph)
library(networkflow)
library(tidygraph)
#devtools::install_github("ParkerICI/vite")
library(vite)
Edges_coupling2<-Basefinale[,-c(4)]
Nodes_coupling2<-desco
Nodes_coupling2$frequence<-1
Nodes_coupling2$Datepubli<-as.character(Nodes_coupling2$Datepubli)
Nodes_coupling2$author_date<-paste(Nodes_coupling2$Cartel,Nodes_coupling2$Firstauthor,Nodes_coupling2$Datepubli)
palette <- c("#1969B3","#01A5D8","#DA3E61","#3CB95F","#E0AF0C","#E25920","#6C7FC9","#DE9493","#CD242E","#6F4288","#B2EEF8","#7FF6FD","#FDB8D6","#8BF9A9","#FEF34A","#FEC57D","#DAEFFB","#FEE3E1","#FBB2A7","#EFD7F2","#5CAADA","#37D4F5","#F5779B","#62E186","#FBDA28","#FB8F4A","#A4B9EA","#FAC2C0","#EB6466","#AD87BC","#0B3074","#00517C","#871B2A","#1A6029","#7C4B05","#8A260E","#2E3679","#793F3F","#840F14","#401C56","#003C65","#741A09","#602A2A","#34134A","#114A1B","#27DDD1","#27DD8D","#4ADD27","#D3DD27","#DDA427","#DF2935","#DD27BC","#BA27DD","#3227DD","#2761DD","#27DDD1")
graph <- tbl_main_component(nodes = Nodes_coupling2, edges = Edges_coupling2, directed = FALSE, node_key = "Cartel", nb_components = 1)
graph <- leiden_workflow(graph, res_1 = 1)
graph <- community_colors(graph, palette, community_column = "Com_ID")
graph <- graph %>%
activate(nodes) %>%
mutate(degree = centrality_degree())
graph <- community_names(graph, ordering_column = "degree", naming = "author_date", community_column = "Com_ID")
graph <- graph %>%
activate(nodes) %>%
mutate(size = degree) # use here what you will use for the size of the nodes in the graph. Force Atlas will take care of avoiding overlapping
graph <- vite::complete_forceatlas2(graph, first.iter = 10000, overlap.method = "repel", overlap.iter = 1000)## Total number of iterations: 10000
## Total number of iterations: 1000graph123<-as.data.frame(graph)
#################################################################################################################
#################################################################################################################
# On extrait les abstracts pour faire de l'analyse textuelle ---------------------------------
#################################################################################################################
#################################################################################################################
load("Data_4_Abstracts.rda")
zalerit$comcitant<-0
for(i in 1:nrow(graph123)){
j<-which(zalerit$Cartel == graph123$Id[i])
zalerit$comcitant[j]<-graph123$Com_ID[i]
}
library(dplyr)
library(janeaustenr) #Inutile je pense
library(tidytext)
library(gutenbergr) #Idem
library(pdftools)
library(tidyverse)
library(qdapRegex)
zaler2<-zalerit[,c(1,3)]
zaler2<-zaler2[zaler2$comcitant!=0,]
zaler2$V1<-as.character(zaler2$V1)
zaler2$comcitant<-as.character(zaler2$comcitant)
paly<-length(table(zaler2$comcitant))
Basetotale_words <- zaler2 %>%
unnest_tokens(word, V1) %>%
count(comcitant, word, sort = TRUE)
# On supprime les mots de deux lettres et de une lettre
Basetotale_words$word<-rm_nchar_words(Basetotale_words$word, 2)
Basetotale_words$word<-rm_nchar_words(Basetotale_words$word, 1)
# On remplace le vide que cree l'etape precedente par des NA et on supprime les NA
Basetotale_words$word[Basetotale_words$word==""] <- NA
Basetotale_words <- na.omit(Basetotale_words)
mystopwords <- tibble(word = c("qmi","consumerswho"))
Basetotale_words <- anti_join(Basetotale_words, mystopwords,
by = "word")
plot_Basetotale <- Basetotale_words %>%
bind_tf_idf(word, comcitant, n) %>%
mutate(word = str_remove_all(word, "_")) %>%
group_by(comcitant) %>%
slice_max(tf_idf, n = 25) %>%
ungroup() %>%
mutate(word = reorder_within(word, tf_idf, comcitant)) %>%
mutate(comcitant = factor(comcitant))
palette <- c("#1969B3","#01A5D8","#DA3E61","#3CB95F","#E0AF0C","#E25920","#6C7FC9","#DE9493","#CD242E","#6F4288","#B2EEF8","#7FF6FD","#FDB8D6","#8BF9A9","#FEF34A","#FEC57D","#DAEFFB","#FEE3E1","#FBB2A7","#EFD7F2","#5CAADA","#37D4F5","#F5779B","#62E186","#FBDA28","#FB8F4A","#A4B9EA","#FAC2C0","#EB6466","#AD87BC","#0B3074","#00517C","#871B2A","#1A6029","#7C4B05","#8A260E","#2E3679","#793F3F","#840F14","#401C56","#003C65","#741A09","#602A2A","#34134A","#114A1B","#27DDD1","#27DD8D","#4ADD27","#D3DD27","#DDA427","#DF2935","#DD27BC","#BA27DD","#3227DD","#2761DD","#27DDD1")
palette <- palette[1:paly]
#ggplot(plot_Basetotale, aes(word, tf_idf, fill = comcitant)) +
# geom_col(show.legend = FALSE) +
# labs(x = NULL, y = "tf-idf") +
# facet_wrap(~comcitant, ncol = 2, scales = "free") +
# coord_flip() +
# scale_x_reordered() +
# scale_fill_manual(values=c(palette))
plot_Basetotale2<-plot_Basetotale[,c(1,2)]
plot_Basetotale2$word<-str_replace(string = plot_Basetotale2$word, pattern = "___.*", replacement = "")
plot_Basetotale2$word<-toupper(plot_Basetotale2$word)
plot_Basetotale2$word<-trimws(plot_Basetotale2$word)
plot_Basetotale2$plural <- gsub('.{1}$','', plot_Basetotale2$word)
j<-unique(plot_Basetotale2$comcitant)
plot_Basetotale2$dede <- 0
for(i in 1: length(unique(plot_Basetotale2$comcitant))){
k<-which(plot_Basetotale2$comcitant==j[i])
for(m in k){if(any(plot_Basetotale2$plural[m]==plot_Basetotale2$word[k])){
z<-which(plot_Basetotale2$plural[m]==plot_Basetotale2$word[k])
if(k[z]<m){
plot_Basetotale2$dede[k[z]]<-10
plot_Basetotale2$dede[m]<-28}else{
plot_Basetotale2$dede[k[z]]<-28
plot_Basetotale2$dede[m]<-10}
}else{
plot_Basetotale2$dede[m]<-plot_Basetotale2$dede[m]}}
}
plot_Basetotale2<-plot_Basetotale2[plot_Basetotale2$dede!=28,]
plot_Basetotale2<-plot_Basetotale2[,c(1,2)]
j<-unique(plot_Basetotale2$comcitant)
plot_Basetotale2$comp<-0
for(i in 1: length(unique(plot_Basetotale2$comcitant))){
k<-which(plot_Basetotale2$comcitant==j[i])[1:4]
plot_Basetotale2$comp[k]<-1
}
plot_Basetotale2<-plot_Basetotale2[plot_Basetotale2$comp==T,]
for(i in 1: length(unique(plot_Basetotale2$comcitant))){
k<-which(plot_Basetotale2$comcitant==j[i])[1]
plot_Basetotale2$comp[k]<-paste(plot_Basetotale2$word[k],";",plot_Basetotale2$word[k+1],";",plot_Basetotale2$word[k+2],";",plot_Basetotale2$word[k+3])
}
plot_Basetotale2<-plot_Basetotale2[,c(1,3)]
plot_Basetotale2<-plot_Basetotale2[plot_Basetotale2$comp!=1,]
names(plot_Basetotale2)[names(plot_Basetotale2) == "comcitant"] <- "Com_ID"
#################################################################################################################################
#################################################################################################################################
# Fin de l'extraction des mots-clés --------------------------------------------------------------
#################################################################################################################################
#################################################################################################################################
top_nodes <- top_nodes(graph, ordering_column = "degree", top_n = 10, top_n_per_com = 1)
community_labels <- community_labels(graph, community_name_column = "Community_name", community_size_column = "Size_com")
community_labels$identifiant<-str_sub (community_labels$Community_name, 1,2)
for(i in 1:nrow(community_labels)){
j<-which(community_labels$identifiant[i] == plot_Basetotale2)
community_labels$Community_name[i]<- plot_Basetotale2$comp[j]
}
library(ggraph)
library(ggrepel)
library(ggnewscale)
ggraph(graph, "manual", x = x, y = y) +
geom_edge_arc(aes(color = color_edges, width = weight), alpha = 0.4, strength = 0.2, show.legend = FALSE) +
scale_edge_width_continuous(range = c(0.1,2)) +
scale_edge_colour_identity() +
geom_node_point(aes(x=x, y=y, size = degree, fill = color), pch = 21, alpha = 0.9, show.legend = FALSE) +
scale_size_continuous(range = c(0.2,13)) +
scale_fill_identity() +
new_scale("size") +
geom_text_repel(data=top_nodes, aes(x=x, y=y, label = Label), size = 2, fontface="bold", alpha = 1, point.padding=NA, show.legend = FALSE) +
geom_label_repel(data=community_labels, aes(x=x, y=y, label = Community_name, fill = color, size = Size_com), fontface="bold", alpha = 0.9, point.padding=NA, show.legend = FALSE) +
scale_size_continuous(range = c(0.5,5)) +
theme_void()
4- Co-citation 2010-2019
##### On a une colonne d'identifiant unique des articles et une colonne avec les refs biblios
##### recodées, on peut donc soumettre ça au package d'Aurelien
set.seed(16)
setwd("C:/Users/jgrad/Desktop/Network_Quality/Network_Quality")
load("Data_5_nodes.rda")
load("Data_7_finalbase.rda")
library(readr)
library(stringr)
#library(bibliometrix)
library(tidyverse)
library(biblionetwork)
#library(devtools)
#devtools::install_github("agoutsmedt/biblionetwork")
eoeoe2<-eoeoe
simple<-unique(eoeoe2$V2)
simple<-as.data.frame(simple)
#setdiff(desco$Cartel,simple[,1])
desco<-desco[desco$Cartel!=108237,]
comp<-cbind(simple,desco$Cartel)
desco$sloubi<-str_detect (string = desco$Datepubli, pattern = "2010|2011|2012|2013|2014|2015|2016|2017|2018|2019")
#desco$sloubi<-str_detect (string = desco$Datepubli, pattern = "1970|1971|1972|1973|1974|1975|1976|1977|1978|1979")
#desco$sloubi<-str_detect (string = desco$Datepubli, pattern = "1980|1981|1982|1983|1984|1985|1986|1987|1988|1989")
#desco$sloubi<-str_detect (string = desco$Datepubli, pattern = "1990|1991|1992|1993|1994|1995|1996|1997|1998|1999")
#desco$sloubi<-str_detect (string = desco$Datepubli, pattern = "2000|2001|2002|2003|2004|2005|2006|2007|2008|2009")
desco<-desco[desco$sloubi==T,]
Cartel<-desco$Cartel
eoeoe2$toofar<-"AB"
for(i in 1:nrow(eoeoe2)){
if(eoeoe2$V2[i]%in%Cartel){eoeoe2$toofar[i]<-T}else{eoeoe2$toofar[i]<-F}}
eoeoe2<-eoeoe2[eoeoe2$toofar==T,]
Carlito<-unique(eoeoe2$V2)
Carlito<-as.data.frame(Carlito)
Cartel<-as.data.frame(Cartel)
Cabal<-cbind(Carlito,Cartel)
eoeoe2$V2<-as.character(eoeoe2$V2)
eoeoe2$identifiant<-as.character(eoeoe2$identifiant)
desco$Cartel<-as.character(desco$Cartel)
Basefinale<-biblio_coupling(eoeoe2, source = "identifiant", ref = "V2", normalized_weight_only = FALSE, weight_threshold = 2)
#devtools::install_github("agoutsmedt/networkflow")
library(biblionetwork)
library(magrittr)
library(dplyr)
library(tidygraph)
library(networkflow)
#devtools::install_github("agoutsmedt/networkflow")
library(tidygraph)
#devtools::install_github("ParkerICI/vite")
library(vite)
Edges_coupling2<-Basefinale[,-c(4)]
Nodes_coupling2<-unique(eoeoe2$identifiant)
Nodes_coupling2<-as.data.frame(Nodes_coupling2)
Nodes_coupling2$frequence<-0
for(i in 1:nrow(Nodes_coupling2)){
j<-which(Nodes_coupling2$Nodes_coupling2[i] == eoeoe2$identifiant)[1]
Nodes_coupling2$frequence[i]<-eoeoe2$frequence[j]
}
Nodes_coupling2$ids<-Nodes_coupling2$Nodes_coupling2
palette <- c("#1969B3","#01A5D8","#DA3E61","#3CB95F","#E0AF0C","#E25920","#6C7FC9","#DE9493","#CD242E","#6F4288","#B2EEF8","#7FF6FD","#FDB8D6","#8BF9A9","#FEF34A","#FEC57D","#DAEFFB","#FEE3E1","#FBB2A7","#EFD7F2","#5CAADA","#37D4F5","#F5779B","#62E186","#FBDA28","#FB8F4A","#A4B9EA","#FAC2C0","#EB6466","#AD87BC","#0B3074","#00517C","#871B2A","#1A6029","#7C4B05","#8A260E","#2E3679","#793F3F","#840F14","#401C56","#003C65","#741A09","#602A2A","#34134A","#114A1B","#27DDD1","#27DD8D","#4ADD27","#D3DD27","#DDA427","#DF2935","#DD27BC","#BA27DD","#3227DD","#2761DD","#27DDD1")
graph <- tbl_main_component(nodes = Nodes_coupling2, edges = Edges_coupling2, directed = FALSE, node_key = "Cartel", nb_components = 1)
## Varying the resolution of the algorithm results in a different partition and
## different number of communities. A lower resolution means less communities, and conversely.
## The basic resolution of the leiden_workflow() is set by res_1 and equals 1 by default.
## You can vary this parameter, but also try a second resolution with res_2 and a third one
## with res_3
## On peut l'enlever avec un argument par défaut de 1
graph <- leiden_workflow(graph, res_1 = 0.5)
graph <- community_colors(graph, palette, community_column = "Com_ID")
graph <- graph %>%
activate(nodes) %>%
mutate(degree = centrality_degree())
graph <- community_names(graph, ordering_column = "degree", naming = "ids", community_column = "Com_ID")
graph <- graph %>%
activate(nodes) %>%
mutate(size = frequence) # use here what you will use for the size of the nodes in the graph. Force Atlas will take care of avoiding overlapping
######## First.iter initiallement 5000, j'ai baissé pour arriver à le faire tourner
graph <- vite::complete_forceatlas2(graph, first.iter = 10000, overlap.method = "repel", overlap.iter = 1500)## Total number of iterations: 10000
## Total number of iterations: 1500top_nodes <- top_nodes(graph, ordering_column = "degree", top_n = 10, top_n_per_com = 1)
community_labels2 <- community_labels(graph, community_name_column = "Community_name", community_size_column = "Size_com")
library(ggraph)
library(ggrepel)
library(ggnewscale)
graph456<-as.data.frame(graph)
ggraph(graph, "manual", x = x, y = y) +
geom_edge_arc(aes(color = color_edges, width = weight), alpha = 0.4, strength = 0.2, show.legend = FALSE) +
scale_edge_width_continuous(range = c(0.1,2)) +
scale_edge_colour_identity() +
geom_node_point(aes(x=x, y=y, size = degree, fill = color), pch = 21, alpha = 0.9, show.legend = FALSE) +
scale_size_continuous(range = c(0.2,13)) +
scale_fill_identity() +
new_scale("size") +
geom_text_repel(data=top_nodes, aes(x=x, y=y, label = Label), size = 2, fontface="bold", alpha = 1, point.padding=NA, show.legend = FALSE) +
geom_label_repel(data=community_labels2, aes(x=x, y=y, label = Community_name, fill = color, size = Size_com), fontface="bold", alpha = 0.9, point.padding=NA, show.legend = FALSE) +
scale_size_continuous(range = c(0.5,5)) +
theme_void()
graph123<-graph123[,c(1,9)]
graph456<-graph456[,c(1,4)]
eoeoe$comcitant<-0
for(i in 1:nrow(graph123)){
j<-which(eoeoe$V2 == graph123$Id[i])
eoeoe$comcitant[j]<-graph123$Com_ID[i]
}
eoeoe$comcite<-0
for(i in 1:nrow(graph456)){
j<-which(eoeoe$identifiant == graph456$Id[i])
eoeoe$comcite[j]<-graph456$Com_ID[i]
}
eoeoe4<-eoeoe
eoeoe4<-eoeoe4[eoeoe4$comcitant!=0,]
eoeoe4<-eoeoe4[eoeoe4$comcite!=0,]
community_labels2$ID<-gsub("-.*", "",community_labels2$Community_name)
community_labels2$IDil<-gsub(".*-", "",community_labels2$Community_name)
community_labels2$IDcompl<-"AB"
for(i in 1:nrow(community_labels2)){
j<-which(community_labels2$IDil[i] == eoeoe$identifiant)
z<-which(paste(eoeoe$Firstauthor[j],eoeoe$Datepubli[j],eoeoe$Issue[j],eoeoe$Page[j])==eoeoe$identifiant[j])[1]
community_labels2$IDcompl[i] <- eoeoe$V1[j[z]]
}
tableau<-as.data.frame.matrix(prop.table(table(eoeoe4$comcitant,eoeoe4$comcite),margin=1)*100)
library(dplyr)
tableau<-tableau %>% mutate_if(is.numeric, ~round(., 1))
community_labels<-community_labels[order(community_labels$identifiant),]
row.names(tableau) <- community_labels$Community_name
community_labels2<-community_labels2[order(community_labels2$Community_name),]
colnames(tableau)<- community_labels2$ID
for(i in 1:nrow(community_labels2)){
community_labels2$IDcompl[i]<-paste(i,"-",community_labels2$IDcompl[i])
}
Legend<-community_labels2[,c(8)]
Legend<-as.data.frame(Legend)
names(Legend)[names(Legend) == "IDcompl"] <- "Complete reference of the clusters"
library(knitr)
library(kableExtra)
kable(Legend)| Complete reference of the clusters |
|---|
| 1 - Melitz, M., The impact of trade on intra-industry reallocations and aggregate industry productivity (2003) Econometrica, 71 (6), pp. 1695-1725; |
| 2 - Corbett, C.J., Montes-Sancho, M.J., Kirsch, D.A., The financial impact of ISO 9000 certification in the United States: an empirical analysis (2005) Manag Sci, 51, pp. 1046-1059; |
| 3 - Akerlof, G.A., The market for” lemons”: Quality uncertainty and the market mechanism (1970) The quarterly journal of economics, 84 (3), pp. 488-500; |
| 4 - Bartley, T., Institutional Emergence in an Era of Globalization: The Rise of Transnational Private Regulation of Labor and Environmental Conditions (2007) American Journal of Sociology, 113, pp. 297-351; |
| 5 - Nelson, P., Information and consumer behavior (1970) The Journal of Political Economy, 78 (2), pp. 311-329; |
| 6 - Kaynak, H., The relationship between total quality management practices and their effects on firm performance (2003) Journal of Operations Management, 21 (4), pp. 405-435; |
| 7 - Jaffee, D., (2007) Brewing justice. Fair trade coffee, sustainability, and survival, , Univ of California Press; |
| 8 - Cachon, G.P., Supply chain coordination with contracts (2003) Handbooks in Operations Research and Management Science: Supply Chain Management, , Graves S, de Kok T, eds, Chap. 6 (Elsevier, Amsterdam); |
| 9 - Maertens, M., Swinnen, J.F.M., Trade, standards, and poverty: evidence from Senegal (2009) World Dev., 37 (1), pp. 161-178; |
| 10 - Tay, A., Assessing Competition in Hospital Care Markets: The Importance of Accounting for Quality Differentiation (2003) RAND Journal of Economics, 34 (4), pp. 786-814; |
| 11 - Zhu, Q., Sarkis, J., Relationships between operational practices and performance among early adopters of green supply chain management practices in Chinese manufacturing enterprises (2004) Journal of Operations Management, 22 (3), pp. 265-289; |
| 12 - Feichtinger, G., Hartl, R.F., Sethi, S.P., Dynamic optimal control models in advertising: Recent developments (1994) Management Science, 40 (2), pp. 195-226; |
| 13 - Lancaster, K., A new approach to consumer theory (1966) Journal of Political Economics, 74, pp. 132-157; |
| 14 - Berry, S., Levinsohn, J., Pakes, A., Automobile Prices in Market Equilibrium (1995) Econometrica, 63, pp. 841-890; |
| 15 - Manova, K., Zhang, Z., China’s exporters and importers: firms, products and trade partners (2009) NBER Working Paper 15249; |
| 16 - Blackburn, J., Scudder, G., Supply chain strategies for perishable products: The case of fresh produce (2009) Production and Operations Management, 18 (2), pp. 129-137; |
| 17 - Zeithaml, V.A., Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence (1988) J. Mark., 52, pp. 2-22 |
| 18 - Li, L., Information sharing in a supply chain with horizontal competition (2002) Management Sci., 48 (9), pp. 1196-1212; |
| 19 - Swann, G.M.P., (2010) The economics of standardization: An update, , Report for the UK Department of Business, Innovation and Skills (BIS); |
| 20 - Arora, A., Fosfuri, A., Licensing the Market for Technology (2003) Journal of Economic Behavior & Organization, 52, pp. 277-295; |
| 21 - Naveh, E., Erez, M., Innovation and attention to detail in the quality improvement paradigm (2004) Manage. Sci., 50 (11), pp. 1576-1586; |
5- Connect the communities 2010-2019
########################################################################################
##############################################################################################################
# Connecter les communautés -----------------------------
##############################################################################################################
##############################################################################################################
tableau %>%
kbl() %>%
kable_styling()| 01 | 02 | 03 | 04 | 05 | 06 | 07 | 08 | 09 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SEARCH ; ADVERTISING ; FIRM ; SELLERS | 2.8 | 1.0 | 47.5 | 0.8 | 27.0 | 0.7 | 0.6 | 2.1 | 0.9 | 1.9 | 0.1 | 2.8 | 2.5 | 6.8 | 0.1 | 0.0 | 0.6 | 1.7 | 0.0 | 0.0 | 0.0 |
| TRADE ; PRODUCTIVITY ; FIRM ; EXPORT | 90.9 | 0.8 | 0.4 | 0.0 | 0.4 | 0.1 | 0.1 | 0.0 | 1.4 | 0.1 | 0.0 | 0.0 | 0.0 | 0.4 | 5.3 | 0.0 | 0.1 | 0.0 | 0.1 | 0.0 | 0.0 |
| ISO ; 14001 ; 9000 ; ENVIRONMENTAL | 0.6 | 60.4 | 0.4 | 4.1 | 1.7 | 23.6 | 0.8 | 0.4 | 0.2 | 0.1 | 7.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.6 |
| COFFEE ; FARMERS ; GOVERNANCE ; FAIR | 5.3 | 2.6 | 0.1 | 17.4 | 1.3 | 0.2 | 46.0 | 0.0 | 27.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.1 |
| SUPPLIER ; CHANNEL ; CONTRACT ; OUTSOURCING | 1.3 | 2.0 | 3.5 | 0.3 | 3.6 | 2.7 | 0.0 | 64.4 | 0.0 | 1.5 | 0.5 | 16.9 | 0.0 | 0.0 | 0.0 | 0.7 | 0.0 | 2.0 | 0.3 | 0.0 | 0.3 |
| PATENTS ; STANDARDISATION ; LICENSING ; STANDARDIZATION | 1.5 | 4.0 | 2.9 | 76.0 | 3.7 | 2.1 | 0.1 | 0.4 | 0.0 | 0.1 | 0.1 | 0.1 | 0.0 | 0.0 | 0.0 | 0.4 | 0.0 | 0.1 | 4.4 | 3.9 | 0.0 |
| LOT ; ITEMS ; DEFECTIVE ; JABER | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 92.3 | 0.0 | 0.0 | 0.0 | 0.0 | 7.7 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| SEAFOOD ; MSC ; SALMON ; FISH | 2.3 | 0.5 | 1.4 | 0.0 | 8.2 | 0.0 | 0.5 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 82.7 | 0.5 | 4.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| HOSPITAL ; LOOP ; PATIENTS ; ALTRUISTIC | 0.3 | 0.0 | 7.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 92.0 | 0.0 | 0.0 | 0.7 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| SERVICE ; TRANSIT ; SATISFACTION ; PASSENGER | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 9.4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 89.6 | 0.0 | 0.0 | 0.0 | 0.0 |
| PERISHABLE ; SHELF ; ARC ; DETERIORATING | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 96.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
6- Bibliographic coupling 2000-2009
#####################################################################################################################
#####################################################################################################################
# Coupling Co-citation 2000-2009 -------------------
#####################################################################################################################
#####################################################################################################################
#####################################################################################################################
#####################################################################################################################
# Bibliographic Coupling 2000-2009----------------------------------------------------
#####################################################################################################################
#####################################################################################################################
##### On a une colonne d'identifiant unique des articles et une colonne avec les refs biblios
##### recodées, on peut donc soumettre ça au package d'Aurelien
set.seed(12)
setwd("C:/Users/jgrad/Desktop/Network_Quality/Network_Quality")
load("Data_5_nodes.rda")
load("Data_7_finalbase.rda")
library(readr)
library(stringr)
#library(bibliometrix)
library(tidyverse)
library(biblionetwork)
#library(devtools)
#devtools::install_github("agoutsmedt/biblionetwork")
eoeoe2<-eoeoe
simple<-unique(eoeoe2$V2)
simple<-as.data.frame(simple)
#setdiff(desco$Cartel,simple[,1])
desco<-desco[desco$Cartel!=108237,]
comp<-cbind(simple,desco$Cartel)
desco$sloubi<-str_detect (string = desco$Datepubli, pattern = "2000|2001|2002|2003|2004|2005|2006|2007|2008|2009")
#desco$sloubi<-str_detect (string = desco$Datepubli, pattern = "1970|1971|1972|1973|1974|1975|1976|1977|1978|1979|1980|1981|1982|1983|1984|1985|1986|1987|1988|1989|1990|1991|1992|1993|1994|1995|1996|1997|1998|1990|2000")
desco<-desco[desco$sloubi==T,]
Cartel<-desco$Cartel
eoeoe2$toofar<-"AB"
for(i in 1:nrow(eoeoe2)){
if(eoeoe2$V2[i]%in%Cartel){eoeoe2$toofar[i]<-T}else{eoeoe2$toofar[i]<-F}}
eoeoe2<-eoeoe2[eoeoe2$toofar==T,]
Carlito<-unique(eoeoe2$V2)
Carlito<-as.data.frame(Carlito)
Cartel<-as.data.frame(Cartel)
Cabal<-cbind(Carlito,Cartel)
eoeoe2$V2<-as.character(eoeoe2$V2)
eoeoe2$identifiant<-as.character(eoeoe2$identifiant)
desco$Cartel<-as.character(desco$Cartel)
Basefinale<-biblio_coupling(eoeoe2, source = "V2", ref = "identifiant", normalized_weight_only = FALSE, weight_threshold = 2)
#devtools::install_github("agoutsmedt/networkflow")
library(biblionetwork)
library(magrittr)
library(dplyr)
library(tidygraph)
library(networkflow)
library(tidygraph)
#devtools::install_github("ParkerICI/vite")
library(vite)
Edges_coupling2<-Basefinale[,-c(4)]
Nodes_coupling2<-desco
Nodes_coupling2$frequence<-1
Nodes_coupling2$Datepubli<-as.character(Nodes_coupling2$Datepubli)
Nodes_coupling2$author_date<-paste(Nodes_coupling2$Cartel,Nodes_coupling2$Firstauthor,Nodes_coupling2$Datepubli)
palette <- c("#1969B3","#01A5D8","#DA3E61","#3CB95F","#E0AF0C","#E25920","#6C7FC9","#DE9493","#CD242E","#6F4288","#B2EEF8","#7FF6FD","#FDB8D6","#8BF9A9","#FEF34A","#FEC57D","#DAEFFB","#FEE3E1","#FBB2A7","#EFD7F2","#5CAADA","#37D4F5","#F5779B","#62E186","#FBDA28","#FB8F4A","#A4B9EA","#FAC2C0","#EB6466","#AD87BC","#0B3074","#00517C","#871B2A","#1A6029","#7C4B05","#8A260E","#2E3679","#793F3F","#840F14","#401C56","#003C65","#741A09","#602A2A","#34134A","#114A1B","#27DDD1","#27DD8D","#4ADD27","#D3DD27","#DDA427","#DF2935","#DD27BC","#BA27DD","#3227DD","#2761DD","#27DDD1")
graph <- tbl_main_component(nodes = Nodes_coupling2, edges = Edges_coupling2, directed = FALSE, node_key = "Cartel", nb_components = 1)
graph <- leiden_workflow(graph, res_1 = 1)
graph <- community_colors(graph, palette, community_column = "Com_ID")
graph <- graph %>%
activate(nodes) %>%
mutate(degree = centrality_degree())
graph <- community_names(graph, ordering_column = "degree", naming = "author_date", community_column = "Com_ID")
graph <- graph %>%
activate(nodes) %>%
mutate(size = degree) # use here what you will use for the size of the nodes in the graph. Force Atlas will take care of avoiding overlapping
graph <- vite::complete_forceatlas2(graph, first.iter = 10000, overlap.method = "repel", overlap.iter = 1000)## Total number of iterations: 7037
## Total number of iterations: 1000graph123<-as.data.frame(graph)
#################################################################################################################
#################################################################################################################
# On extrait les abstracts pour faire de l'analyse textuelle ---------------------------------
#################################################################################################################
#################################################################################################################
load("Data_4_Abstracts.rda")
zalerit$comcitant<-0
for(i in 1:nrow(graph123)){
j<-which(zalerit$Cartel == graph123$Id[i])
zalerit$comcitant[j]<-graph123$Com_ID[i]
}
library(dplyr)
library(janeaustenr) #Inutile je pense
library(tidytext)
library(gutenbergr) #Idem
library(pdftools)
library(tidyverse)
library(qdapRegex)
zaler2<-zalerit[,c(1,3)]
zaler2<-zaler2[zaler2$comcitant!=0,]
zaler2$V1<-as.character(zaler2$V1)
zaler2$comcitant<-as.character(zaler2$comcitant)
paly<-length(table(zaler2$comcitant))
Basetotale_words <- zaler2 %>%
unnest_tokens(word, V1) %>%
count(comcitant, word, sort = TRUE)
# On supprime les mots de deux lettres et de une lettre
Basetotale_words$word<-rm_nchar_words(Basetotale_words$word, 2)
Basetotale_words$word<-rm_nchar_words(Basetotale_words$word, 1)
# On remplace le vide que cree l'etape precedente par des NA et on supprime les NA
Basetotale_words$word[Basetotale_words$word==""] <- NA
Basetotale_words <- na.omit(Basetotale_words)
mystopwords <- tibble(word = c("qmi","consumerswho"))
Basetotale_words <- anti_join(Basetotale_words, mystopwords,
by = "word")
plot_Basetotale <- Basetotale_words %>%
bind_tf_idf(word, comcitant, n) %>%
mutate(word = str_remove_all(word, "_")) %>%
group_by(comcitant) %>%
slice_max(tf_idf, n = 25) %>%
ungroup() %>%
mutate(word = reorder_within(word, tf_idf, comcitant)) %>%
mutate(comcitant = factor(comcitant))
palette <- c("#1969B3","#01A5D8","#DA3E61","#3CB95F","#E0AF0C","#E25920","#6C7FC9","#DE9493","#CD242E","#6F4288","#B2EEF8","#7FF6FD","#FDB8D6","#8BF9A9","#FEF34A","#FEC57D","#DAEFFB","#FEE3E1","#FBB2A7","#EFD7F2","#5CAADA","#37D4F5","#F5779B","#62E186","#FBDA28","#FB8F4A","#A4B9EA","#FAC2C0","#EB6466","#AD87BC","#0B3074","#00517C","#871B2A","#1A6029","#7C4B05","#8A260E","#2E3679","#793F3F","#840F14","#401C56","#003C65","#741A09","#602A2A","#34134A","#114A1B","#27DDD1","#27DD8D","#4ADD27","#D3DD27","#DDA427","#DF2935","#DD27BC","#BA27DD","#3227DD","#2761DD","#27DDD1")
palette <- palette[1:paly]
#ggplot(plot_Basetotale, aes(word, tf_idf, fill = comcitant)) +
# geom_col(show.legend = FALSE) +
# labs(x = NULL, y = "tf-idf") +
# facet_wrap(~comcitant, ncol = 2, scales = "free") +
# coord_flip() +
# scale_x_reordered() +
# scale_fill_manual(values=c(palette))
plot_Basetotale2<-plot_Basetotale[,c(1,2)]
plot_Basetotale2$word<-str_replace(string = plot_Basetotale2$word, pattern = "___.*", replacement = "")
plot_Basetotale2$word<-toupper(plot_Basetotale2$word)
plot_Basetotale2$word<-trimws(plot_Basetotale2$word)
plot_Basetotale2$plural <- gsub('.{1}$','', plot_Basetotale2$word)
j<-unique(plot_Basetotale2$comcitant)
plot_Basetotale2$dede <- 0
for(i in 1: length(unique(plot_Basetotale2$comcitant))){
k<-which(plot_Basetotale2$comcitant==j[i])
for(m in k){if(any(plot_Basetotale2$plural[m]==plot_Basetotale2$word[k])){
z<-which(plot_Basetotale2$plural[m]==plot_Basetotale2$word[k])
if(k[z]<m){
plot_Basetotale2$dede[k[z]]<-10
plot_Basetotale2$dede[m]<-28}else{
plot_Basetotale2$dede[k[z]]<-28
plot_Basetotale2$dede[m]<-10}
}else{
plot_Basetotale2$dede[m]<-plot_Basetotale2$dede[m]}}
}
plot_Basetotale2<-plot_Basetotale2[plot_Basetotale2$dede!=28,]
plot_Basetotale2<-plot_Basetotale2[,c(1,2)]
j<-unique(plot_Basetotale2$comcitant)
plot_Basetotale2$comp<-0
for(i in 1: length(unique(plot_Basetotale2$comcitant))){
k<-which(plot_Basetotale2$comcitant==j[i])[1:4]
plot_Basetotale2$comp[k]<-1
}
plot_Basetotale2<-plot_Basetotale2[plot_Basetotale2$comp==T,]
for(i in 1: length(unique(plot_Basetotale2$comcitant))){
k<-which(plot_Basetotale2$comcitant==j[i])[1]
plot_Basetotale2$comp[k]<-paste(plot_Basetotale2$word[k],";",plot_Basetotale2$word[k+1],";",plot_Basetotale2$word[k+2],";",plot_Basetotale2$word[k+3])
}
plot_Basetotale2<-plot_Basetotale2[,c(1,3)]
plot_Basetotale2<-plot_Basetotale2[plot_Basetotale2$comp!=1,]
names(plot_Basetotale2)[names(plot_Basetotale2) == "comcitant"] <- "Com_ID"
#################################################################################################################################
#################################################################################################################################
# Fin de l'extraction des mots-clés --------------------------------------------------------------
#################################################################################################################################
#################################################################################################################################
top_nodes <- top_nodes(graph, ordering_column = "degree", top_n = 10, top_n_per_com = 1)
community_labels <- community_labels(graph, community_name_column = "Community_name", community_size_column = "Size_com")
community_labels$identifiant<-str_sub (community_labels$Community_name, 1,2)
for(i in 1:nrow(community_labels)){
j<-which(community_labels$identifiant[i] == plot_Basetotale2)
community_labels$Community_name[i]<- plot_Basetotale2$comp[j]
}
library(ggraph)
library(ggrepel)
library(ggnewscale)
ggraph(graph, "manual", x = x, y = y) +
geom_edge_arc(aes(color = color_edges, width = weight), alpha = 0.4, strength = 0.2, show.legend = FALSE) +
scale_edge_width_continuous(range = c(0.1,2)) +
scale_edge_colour_identity() +
geom_node_point(aes(x=x, y=y, size = degree, fill = color), pch = 21, alpha = 0.9, show.legend = FALSE) +
scale_size_continuous(range = c(0.2,13)) +
scale_fill_identity() +
new_scale("size") +
geom_text_repel(data=top_nodes, aes(x=x, y=y, label = Label), size = 2, fontface="bold", alpha = 1, point.padding=NA, show.legend = FALSE) +
geom_label_repel(data=community_labels, aes(x=x, y=y, label = Community_name, fill = color, size = Size_com), fontface="bold", alpha = 0.9, point.padding=NA, show.legend = FALSE) +
scale_size_continuous(range = c(0.5,5)) +
theme_void()
7-Cocitation 2000-2009
##### On a une colonne d'identifiant unique des articles et une colonne avec les refs biblios
##### recodées, on peut donc soumettre ça au package d'Aurelien
set.seed(12)
setwd("C:/Users/jgrad/Desktop/Network_Quality/Network_Quality")
load("Data_5_nodes.rda")
load("Data_7_finalbase.rda")
library(readr)
library(stringr)
#library(bibliometrix)
library(tidyverse)
library(biblionetwork)
#library(devtools)
#devtools::install_github("agoutsmedt/biblionetwork")
eoeoe2<-eoeoe
simple<-unique(eoeoe2$V2)
simple<-as.data.frame(simple)
#setdiff(desco$Cartel,simple[,1])
desco<-desco[desco$Cartel!=108237,]
comp<-cbind(simple,desco$Cartel)
desco$sloubi<-str_detect (string = desco$Datepubli, pattern = "2000|2001|2002|2003|2004|2005|2006|2007|2008|2009")
#desco$sloubi<-str_detect (string = desco$Datepubli, pattern = "1970|1971|1972|1973|1974|1975|1976|1977|1978|1979")
#desco$sloubi<-str_detect (string = desco$Datepubli, pattern = "1980|1981|1982|1983|1984|1985|1986|1987|1988|1989")
#desco$sloubi<-str_detect (string = desco$Datepubli, pattern = "1990|1991|1992|1993|1994|1995|1996|1997|1998|1999")
#desco$sloubi<-str_detect (string = desco$Datepubli, pattern = "2000|2001|2002|2003|2004|2005|2006|2007|2008|2009")
desco<-desco[desco$sloubi==T,]
Cartel<-desco$Cartel
eoeoe2$toofar<-"AB"
for(i in 1:nrow(eoeoe2)){
if(eoeoe2$V2[i]%in%Cartel){eoeoe2$toofar[i]<-T}else{eoeoe2$toofar[i]<-F}}
eoeoe2<-eoeoe2[eoeoe2$toofar==T,]
Carlito<-unique(eoeoe2$V2)
Carlito<-as.data.frame(Carlito)
Cartel<-as.data.frame(Cartel)
Cabal<-cbind(Carlito,Cartel)
eoeoe2$V2<-as.character(eoeoe2$V2)
eoeoe2$identifiant<-as.character(eoeoe2$identifiant)
desco$Cartel<-as.character(desco$Cartel)
Basefinale<-biblio_coupling(eoeoe2, source = "identifiant", ref = "V2", normalized_weight_only = FALSE, weight_threshold = 2)
#devtools::install_github("agoutsmedt/networkflow")
library(biblionetwork)
library(magrittr)
library(dplyr)
library(tidygraph)
library(networkflow)
#devtools::install_github("agoutsmedt/networkflow")
library(tidygraph)
#devtools::install_github("ParkerICI/vite")
library(vite)
Edges_coupling2<-Basefinale[,-c(4)]
Nodes_coupling2<-unique(eoeoe2$identifiant)
Nodes_coupling2<-as.data.frame(Nodes_coupling2)
Nodes_coupling2$frequence<-0
for(i in 1:nrow(Nodes_coupling2)){
j<-which(Nodes_coupling2$Nodes_coupling2[i] == eoeoe2$identifiant)[1]
Nodes_coupling2$frequence[i]<-eoeoe2$frequence[j]
}
Nodes_coupling2$ids<-Nodes_coupling2$Nodes_coupling2
palette <- c("#1969B3","#01A5D8","#DA3E61","#3CB95F","#E0AF0C","#E25920","#6C7FC9","#DE9493","#CD242E","#6F4288","#B2EEF8","#7FF6FD","#FDB8D6","#8BF9A9","#FEF34A","#FEC57D","#DAEFFB","#FEE3E1","#FBB2A7","#EFD7F2","#5CAADA","#37D4F5","#F5779B","#62E186","#FBDA28","#FB8F4A","#A4B9EA","#FAC2C0","#EB6466","#AD87BC","#0B3074","#00517C","#871B2A","#1A6029","#7C4B05","#8A260E","#2E3679","#793F3F","#840F14","#401C56","#003C65","#741A09","#602A2A","#34134A","#114A1B","#27DDD1","#27DD8D","#4ADD27","#D3DD27","#DDA427","#DF2935","#DD27BC","#BA27DD","#3227DD","#2761DD","#27DDD1")
graph <- tbl_main_component(nodes = Nodes_coupling2, edges = Edges_coupling2, directed = FALSE, node_key = "Cartel", nb_components = 1)
## Varying the resolution of the algorithm results in a different partition and
## different number of communities. A lower resolution means less communities, and conversely.
## The basic resolution of the leiden_workflow() is set by res_1 and equals 1 by default.
## You can vary this parameter, but also try a second resolution with res_2 and a third one
## with res_3
## On peut l'enlever avec un argument par défaut de 1
graph <- leiden_workflow(graph, res_1 = 0.5)
graph <- community_colors(graph, palette, community_column = "Com_ID")
graph <- graph %>%
activate(nodes) %>%
mutate(degree = centrality_degree())
graph <- community_names(graph, ordering_column = "degree", naming = "ids", community_column = "Com_ID")
graph <- graph %>%
activate(nodes) %>%
mutate(size = frequence) # use here what you will use for the size of the nodes in the graph. Force Atlas will take care of avoiding overlapping
######## First.iter initiallement 5000, j'ai baissé pour arriver à le faire tourner
graph <- vite::complete_forceatlas2(graph, first.iter = 10000, overlap.method = "repel", overlap.iter = 1500)## Total number of iterations: 10000
## Total number of iterations: 1500top_nodes <- top_nodes(graph, ordering_column = "degree", top_n = 10, top_n_per_com = 1)
community_labels2 <- community_labels(graph, community_name_column = "Community_name", community_size_column = "Size_com")
library(ggraph)
library(ggrepel)
library(ggnewscale)
graph456<-as.data.frame(graph)
ggraph(graph, "manual", x = x, y = y) +
geom_edge_arc(aes(color = color_edges, width = weight), alpha = 0.4, strength = 0.2, show.legend = FALSE) +
scale_edge_width_continuous(range = c(0.1,2)) +
scale_edge_colour_identity() +
geom_node_point(aes(x=x, y=y, size = degree, fill = color), pch = 21, alpha = 0.9, show.legend = FALSE) +
scale_size_continuous(range = c(0.2,13)) +
scale_fill_identity() +
new_scale("size") +
geom_text_repel(data=top_nodes, aes(x=x, y=y, label = Label), size = 2, fontface="bold", alpha = 1, point.padding=NA, show.legend = FALSE) +
geom_label_repel(data=community_labels2, aes(x=x, y=y, label = Community_name, fill = color, size = Size_com), fontface="bold", alpha = 0.9, point.padding=NA, show.legend = FALSE) +
scale_size_continuous(range = c(0.5,5)) +
theme_void()
graph123<-graph123[,c(1,9)]
graph456<-graph456[,c(1,4)]
eoeoe$comcitant<-0
for(i in 1:nrow(graph123)){
j<-which(eoeoe$V2 == graph123$Id[i])
eoeoe$comcitant[j]<-graph123$Com_ID[i]
}
eoeoe$comcite<-0
for(i in 1:nrow(graph456)){
j<-which(eoeoe$identifiant == graph456$Id[i])
eoeoe$comcite[j]<-graph456$Com_ID[i]
}
eoeoe4<-eoeoe
eoeoe4<-eoeoe4[eoeoe4$comcitant!=0,]
eoeoe4<-eoeoe4[eoeoe4$comcite!=0,]
community_labels2$ID<-gsub("-.*", "",community_labels2$Community_name)
community_labels2$IDil<-gsub(".*-", "",community_labels2$Community_name)
community_labels2$IDcompl<-"AB"
for(i in 1:nrow(community_labels2)){
j<-which(community_labels2$IDil[i] == eoeoe$identifiant)
z<-which(paste(eoeoe$Firstauthor[j],eoeoe$Datepubli[j],eoeoe$Issue[j],eoeoe$Page[j])==eoeoe$identifiant[j])[1]
community_labels2$IDcompl[i] <- eoeoe$V1[j[z]]
}
tableau<-as.data.frame.matrix(prop.table(table(eoeoe4$comcitant,eoeoe4$comcite),margin=1)*100)
library(dplyr)
tableau<-tableau %>% mutate_if(is.numeric, ~round(., 1))
community_labels<-community_labels[order(community_labels$identifiant),]
row.names(tableau) <- community_labels$Community_name
community_labels2<-community_labels2[order(community_labels2$Community_name),]
colnames(tableau)<- community_labels2$ID
for(i in 1:nrow(community_labels2)){
community_labels2$IDcompl[i]<-paste(i,"-",community_labels2$IDcompl[i])
}
Legend<-community_labels2[,c(8)]
Legend<-as.data.frame(Legend)
names(Legend)[names(Legend) == "IDcompl"] <- "Complete reference of the clusters"
library(knitr)
library(kableExtra)
kable(Legend)| Complete reference of the clusters |
|---|
| 1 - Flynn, B.B., Schroeder, R.G., Sakakibara, S., A framework for quality management research and an associated measurement instrument (1994) J. Oper. Manag., 11 (4), pp. 339-366; |
| 2 - Akerlof, G.A., The market for” lemons”: Quality uncertainty and the market mechanism (1970) The quarterly journal of economics, 84 (3), pp. 488-500; |
| 3 - DiMaggio, P.J., Powell, W.W., The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields (1983) American Sociological Review, 48 (2), pp. 147-160; |
| 4 - Mussa, M., Rosen, S., Monopoly and product quality (1978) Journal of Economic theory, 18, pp. 301-317; |
| 5 - Williamson, O.E., (1985) The Economic Institutions of Capitalism, , Free Press, New York; |
| 6 - Parasuraman, A., Berry, L.L., Zeithaml, V.A., Zeithaml, V.A., Berry, L.L., SERVQUAL: Multiple-item scale for measuring consumer perceptions of service quality (1988) Journal of Retailing, 64 (1), pp. 12-40; |
| 7 - Darby, M., Karni, E., Free competition and the optimal amount of fraud (1973) Journal of Law and Economics, 16, pp. 67-88; |
| 8 - Gereffi, G., Garcia-Johnson, R., Sasser, E., The NGO-industrial complex (2001) Foreign Policy July/Aug, pp. 56-65; |
| 9 - Spence, A.M., Monopoly, quality, and regulation (1975) Bell Journal of Economics, 6 (2), pp. 417-429; |
| 10 - Eliashberg, J., Shugan, S.M., Film Critics: Influencers or Predictors? (1997) Journal of Marketing, 61 (2), pp. 68-78; |
| 11 - Dellavigna, S., Malmendier, U., Contract Design and Self-Control: Theory and Evidence (2004) Quarterly Journal of Economics, 119, pp. 353-402; |
| 12 - Abrahamson, E., Managerial fads and fashions: The diffusion and rejection of innovations (1991) Acad. Manag. Rev., 16 (3), pp. 586-612; |
| 13 - Rosenbaum, P., Rubin, D., The central role of the propensity score in observational studies for causal effects (1983) Biometrika, 70, pp. 41-55; |
| 14 - Beaulieu, N.D., Quality information and consumer health plan choices (2002) Journal of Health Economics, 21 (1), pp. 43-63., DOI 10.1016/S0167-6296(01)00126-6, PII S0167629601001266; |
| 15 - Arrow, K., Economic Welfare and the Allocation of Resources for Inventions (1962) The Rate and Direction of Inventive Activity: Economic and Social Factors, , R. Nelson, ed., Princeton, NJ: Princeton University Press; |
| 16 - Becker, G.S., A note on restaurant pricing and other examples of social influences on price (1991) Journal of Political Economy, 99 (5), pp. 1109-1116; |
| 17 - Funk, J.L., Methe, D.T., Market- and committee-based mechanisms in the creation and diffusion of global industry standards: The case of mobile communication (2001) Research Policy, 30 (4), pp. 589-610., DOI 10.1016/S0048-7333(00)00095-0, PII S0048733300000950; |
| 18 - Poksinska, B., Dahlgaard, J.J., Eklund, J.A., Implementing ISO 14000 in Sweden: motives, benefits and comparisons with ISO 9000 (2003) International Journal of Quality and Reliability Management, 20, pp. 585-606; |
| 19 - Maddala, G.S., Wu, S., A comparative study of unit root tests with panel data and a new simple test (1999) Oxford Bulletin of Economics and Statistics, 61, pp. 631-652; |
| 20 - Salameh, M., Jaber, M., Economic production quantity model for items with imperfect quality (2000) International Journal of Production Economics, 64 (13), pp. 59-64; |
| 21 - Takeyama, L., The welfare implications of unauthorized reproduction of intellectual property in the presence of demand network externalities (1994) Journal of Industrial Economics, 42, pp. 155-166; |
| 22 - Seddon, J., (2000) The Case against ISO 9000, , Dublin: Oak Tree Press; |
| 23 - Garrard, G.A., (1998) Cellular Communications: World-Wide Market Development, , Artech House Norwood, MA, USA; |
| 24 - Withers, B.E., Ebrahimpour, M., Hikmet, N., An exploration of the impact of TQM and JIT on ISO 9000 registered firms (1997) International Journal of Production Economics, 53 (2), pp. 209-216; |
| 25 - Hofstede, G., (1980) Culture’s Consequences: International Differences in Work-related Values, 5., Sage Publications, Inc; |
| 26 - Gans, N., Customer loyalty and supplier quality competition (2002) Management Science, 48 (2), pp. 207-221; |
| 27 - Sykes, A.O., (1995) Product Standards for Internationally Integrated Goods Markets, , Washington, DC: The Brookings Institution; |
| 28 - Deaton, A.S., Muellbauer, J., An Almost Ideal Demand System (1980) American Economic Review, 70, pp. 312-326; |
| 29 - Anupindi, R., Akella, R., Diversification under supply uncertainty (1993) Management Science, 39 (8), pp. 944-963; |
| 30 - Zaibet, L., Bredhal, M., Gains from ISO Certification in the UK Meat Sector (1997) Agribusiness, 13 (4), pp. 375-384 |
| 31 - Heide, J.B., John, G., The role of dependence balancing in safeguarding transaction-specific assets in conventional channels (1988) J. Marketing, 52 (1), pp. 20-35; |
| 32 - Kothari, S.P., Warner, J.B., Measuring long-horizon security price performance (1997) Journal of Financial Economics, 43, pp. 301-339; |
| 33 - Kyle, A.S., Continuous auctions and insider trading (1985) Econometrica, 53 (6), pp. 1315-1335; |
| 34 - Hamilton, J.T., (2004) All the News That’s Fit to Sell:.How the Market Transforms Information Into News, , Princeton: Princeton University Press; |
| 35 - Hedges, L., Olkin, I., (1985) Statistical Methods for Meta-Analysis, , San Diego, CA: Academic Press, Ltd; |
8- Connect the communities 2000-2009
########################################################################################
##############################################################################################################
# Connecter les communautés -----------------------------
##############################################################################################################
##############################################################################################################
tableau %>%
kbl() %>%
kable_styling()| 01 | 02 | 03 | 04 | 05 | 06 | 07 | 08 | 09 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BEEF ; HYPOTHETICAL ; WINE ; REPUTATION | 1.2 | 41.0 | 2.1 | 2.6 | 1.6 | 1.2 | 42.2 | 1.9 | 1.3 | 0.7 | 0.7 | 0.1 | 0.0 | 0.1 | 0.0 | 2.2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7 | 0.0 | 0.2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| HOSPITAL ; QUALITY ; MEDICARE ; PLAN | 0.5 | 8.1 | 1.2 | 36.3 | 3.9 | 0.1 | 16.5 | 0.2 | 19.0 | 1.4 | 2.9 | 0.0 | 0.1 | 7.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 1.8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4 | 0.0 |
| TQM ; ISO ; 9000 ; SIGMA | 87.2 | 0.5 | 3.3 | 0.4 | 0.2 | 2.0 | 0.1 | 0.6 | 0.0 | 0.2 | 0.0 | 4.8 | 0.1 | 0.0 | 0.0 | 0.0 | 0.1 | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.1 | 0.0 | 0.0 | 0.0 |
| 14001 ; ISO ; EMS ; GSCM | 14.6 | 1.2 | 65.4 | 2.8 | 2.1 | 0.4 | 1.0 | 3.7 | 0.2 | 1.2 | 0.5 | 2.3 | 0.2 | 0.0 | 0.3 | 0.0 | 0.1 | 2.4 | 0.1 | 0.0 | 0.0 | 0.8 | 0.0 | 0.0 | 0.1 | 0.0 | 0.1 | 0.0 | 0.0 | 0.3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 |
| STANDARDIZATION ; NETWORK ; STANDARDS ; GSM | 2.3 | 5.6 | 1.0 | 62.4 | 3.1 | 0.8 | 0.6 | 0.5 | 0.6 | 5.5 | 3.3 | 0.4 | 0.0 | 0.0 | 0.4 | 3.2 | 5.6 | 0.0 | 0.0 | 0.0 | 0.2 | 0.0 | 1.5 | 1.1 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5 | 0.2 | 0.0 |
| DISCLOSURE ; ADVERTISING ; DISCLOSE ; SPOT | 0.7 | 55.1 | 0.6 | 16.0 | 0.8 | 1.7 | 1.9 | 0.0 | 0.5 | 15.8 | 5.0 | 0.4 | 0.0 | 0.7 | 0.0 | 0.8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| TRADE ; PLANTS ; PRODUCTIVITY ; EXPORTING | 0.4 | 1.5 | 1.6 | 2.7 | 81.5 | 0.0 | 1.0 | 1.2 | 7.3 | 0.8 | 0.0 | 0.2 | 0.6 | 0.0 | 0.6 | 0.0 | 0.0 | 0.0 | 0.3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2 | 0.0 | 0.0 | 0.0 |
| SERVQUAL ; SATISFACTION ; INTENTIONS ; SERVICE | 12.9 | 9.3 | 0.6 | 1.2 | 0.2 | 72.0 | 1.2 | 0.1 | 0.0 | 0.3 | 0.1 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.1 |
| MANUFACTURER ; SHARING ; CHANNEL ; SUPPLIER | 4.0 | 55.1 | 0.9 | 3.1 | 6.3 | 0.8 | 0.4 | 0.0 | 4.1 | 0.4 | 12.6 | 0.4 | 0.0 | 0.0 | 0.2 | 0.3 | 0.0 | 0.0 | 0.0 | 6.7 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.2 | 0.0 | 0.0 | 1.8 | 0.0 | 0.5 | 0.4 | 0.0 | 0.0 | 0.0 |
| COFFEE ; FARMERS ; FAIR ; STANDARDS | 1.1 | 1.2 | 2.2 | 0.5 | 3.9 | 0.3 | 3.6 | 71.2 | 0.0 | 0.4 | 0.1 | 0.1 | 14.7 | 0.0 | 0.0 | 0.2 | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| LICENSING ; ROYALTY ; DRASTIC ; OUTSIDER | 1.7 | 2.6 | 0.4 | 4.3 | 2.6 | 0.0 | 0.4 | 1.3 | 0.4 | 1.3 | 0.0 | 0.0 | 0.0 | 0.0 | 84.3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| STATISTIC ; ROOT ; AUGMENTED ; RANK | 1.5 | 0.0 | 4.4 | 2.9 | 1.5 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 89.7 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
9- Bibliographic coupling 1990-1999
#####################################################################################################################
#####################################################################################################################
# Coupling Co-citation 2000-2009 -------------------
#####################################################################################################################
#####################################################################################################################
#####################################################################################################################
#####################################################################################################################
# Bibliographic Coupling 2000-2009----------------------------------------------------
#####################################################################################################################
#####################################################################################################################
##### On a une colonne d'identifiant unique des articles et une colonne avec les refs biblios
##### recodées, on peut donc soumettre ça au package d'Aurelien
set.seed(12)
setwd("C:/Users/jgrad/Desktop/Network_Quality/Network_Quality")
load("Data_5_nodes.rda")
load("Data_7_finalbase.rda")
library(readr)
library(stringr)
#library(bibliometrix)
library(tidyverse)
library(biblionetwork)
#library(devtools)
#devtools::install_github("agoutsmedt/biblionetwork")
eoeoe2<-eoeoe
simple<-unique(eoeoe2$V2)
simple<-as.data.frame(simple)
#setdiff(desco$Cartel,simple[,1])
desco<-desco[desco$Cartel!=108237,]
comp<-cbind(simple,desco$Cartel)
desco$sloubi<-str_detect (string = desco$Datepubli, pattern = "1990|1991|1992|1993|1994|1995|1996|1997|1998|1999")
#desco$sloubi<-str_detect (string = desco$Datepubli, pattern = "1970|1971|1972|1973|1974|1975|1976|1977|1978|1979|1980|1981|1982|1983|1984|1985|1986|1987|1988|1989|1990|1991|1992|1993|1994|1995|1996|1997|1998|1990|2000")
desco<-desco[desco$sloubi==T,]
Cartel<-desco$Cartel
eoeoe2$toofar<-"AB"
for(i in 1:nrow(eoeoe2)){
if(eoeoe2$V2[i]%in%Cartel){eoeoe2$toofar[i]<-T}else{eoeoe2$toofar[i]<-F}}
eoeoe2<-eoeoe2[eoeoe2$toofar==T,]
Carlito<-unique(eoeoe2$V2)
Carlito<-as.data.frame(Carlito)
Cartel<-as.data.frame(Cartel)
Cabal<-cbind(Carlito,Cartel)
eoeoe2$V2<-as.character(eoeoe2$V2)
eoeoe2$identifiant<-as.character(eoeoe2$identifiant)
desco$Cartel<-as.character(desco$Cartel)
Basefinale<-biblio_coupling(eoeoe2, source = "V2", ref = "identifiant", normalized_weight_only = FALSE, weight_threshold = 2)
#devtools::install_github("agoutsmedt/networkflow")
library(biblionetwork)
library(magrittr)
library(dplyr)
library(tidygraph)
library(networkflow)
library(tidygraph)
#devtools::install_github("ParkerICI/vite")
library(vite)
Edges_coupling2<-Basefinale[,-c(4)]
Nodes_coupling2<-desco
Nodes_coupling2$frequence<-1
Nodes_coupling2$Datepubli<-as.character(Nodes_coupling2$Datepubli)
Nodes_coupling2$author_date<-paste(Nodes_coupling2$Cartel,Nodes_coupling2$Firstauthor,Nodes_coupling2$Datepubli)
palette <- c("#1969B3","#01A5D8","#DA3E61","#3CB95F","#E0AF0C","#E25920","#6C7FC9","#DE9493","#CD242E","#6F4288","#B2EEF8","#7FF6FD","#FDB8D6","#8BF9A9","#FEF34A","#FEC57D","#DAEFFB","#FEE3E1","#FBB2A7","#EFD7F2","#5CAADA","#37D4F5","#F5779B","#62E186","#FBDA28","#FB8F4A","#A4B9EA","#FAC2C0","#EB6466","#AD87BC","#0B3074","#00517C","#871B2A","#1A6029","#7C4B05","#8A260E","#2E3679","#793F3F","#840F14","#401C56","#003C65","#741A09","#602A2A","#34134A","#114A1B","#27DDD1","#27DD8D","#4ADD27","#D3DD27","#DDA427","#DF2935","#DD27BC","#BA27DD","#3227DD","#2761DD","#27DDD1")
graph <- tbl_main_component(nodes = Nodes_coupling2, edges = Edges_coupling2, directed = FALSE, node_key = "Cartel", nb_components = 1)
graph <- leiden_workflow(graph, res_1 = 1)
graph <- community_colors(graph, palette, community_column = "Com_ID")
graph <- graph %>%
activate(nodes) %>%
mutate(degree = centrality_degree())
graph <- community_names(graph, ordering_column = "degree", naming = "author_date", community_column = "Com_ID")
graph <- graph %>%
activate(nodes) %>%
mutate(size = degree) # use here what you will use for the size of the nodes in the graph. Force Atlas will take care of avoiding overlapping
graph <- vite::complete_forceatlas2(graph, first.iter = 10000, overlap.method = "repel", overlap.iter = 1000)## Total number of iterations: 3453
## Total number of iterations: 1000graph123<-as.data.frame(graph)
#################################################################################################################
#################################################################################################################
# On extrait les abstracts pour faire de l'analyse textuelle ---------------------------------
#################################################################################################################
#################################################################################################################
load("Data_4_Abstracts.rda")
zalerit$comcitant<-0
for(i in 1:nrow(graph123)){
j<-which(zalerit$Cartel == graph123$Id[i])
zalerit$comcitant[j]<-graph123$Com_ID[i]
}
library(dplyr)
library(janeaustenr) #Inutile je pense
library(tidytext)
library(gutenbergr) #Idem
library(pdftools)
library(tidyverse)
library(qdapRegex)
zaler2<-zalerit[,c(1,3)]
zaler2<-zaler2[zaler2$comcitant!=0,]
zaler2$V1<-as.character(zaler2$V1)
zaler2$comcitant<-as.character(zaler2$comcitant)
paly<-length(table(zaler2$comcitant))
Basetotale_words <- zaler2 %>%
unnest_tokens(word, V1) %>%
count(comcitant, word, sort = TRUE)
# On supprime les mots de deux lettres et de une lettre
Basetotale_words$word<-rm_nchar_words(Basetotale_words$word, 2)
Basetotale_words$word<-rm_nchar_words(Basetotale_words$word, 1)
# On remplace le vide que cree l'etape precedente par des NA et on supprime les NA
Basetotale_words$word[Basetotale_words$word==""] <- NA
Basetotale_words <- na.omit(Basetotale_words)
mystopwords <- tibble(word = c("qmi","consumerswho"))
Basetotale_words <- anti_join(Basetotale_words, mystopwords,
by = "word")
plot_Basetotale <- Basetotale_words %>%
bind_tf_idf(word, comcitant, n) %>%
mutate(word = str_remove_all(word, "_")) %>%
group_by(comcitant) %>%
slice_max(tf_idf, n = 25) %>%
ungroup() %>%
mutate(word = reorder_within(word, tf_idf, comcitant)) %>%
mutate(comcitant = factor(comcitant))
palette <- c("#1969B3","#01A5D8","#DA3E61","#3CB95F","#E0AF0C","#E25920","#6C7FC9","#DE9493","#CD242E","#6F4288","#B2EEF8","#7FF6FD","#FDB8D6","#8BF9A9","#FEF34A","#FEC57D","#DAEFFB","#FEE3E1","#FBB2A7","#EFD7F2","#5CAADA","#37D4F5","#F5779B","#62E186","#FBDA28","#FB8F4A","#A4B9EA","#FAC2C0","#EB6466","#AD87BC","#0B3074","#00517C","#871B2A","#1A6029","#7C4B05","#8A260E","#2E3679","#793F3F","#840F14","#401C56","#003C65","#741A09","#602A2A","#34134A","#114A1B","#27DDD1","#27DD8D","#4ADD27","#D3DD27","#DDA427","#DF2935","#DD27BC","#BA27DD","#3227DD","#2761DD","#27DDD1")
palette <- palette[1:paly]
#ggplot(plot_Basetotale, aes(word, tf_idf, fill = comcitant)) +
# geom_col(show.legend = FALSE) +
# labs(x = NULL, y = "tf-idf") +
# facet_wrap(~comcitant, ncol = 2, scales = "free") +
# coord_flip() +
# scale_x_reordered() +
# scale_fill_manual(values=c(palette))
plot_Basetotale2<-plot_Basetotale[,c(1,2)]
plot_Basetotale2$word<-str_replace(string = plot_Basetotale2$word, pattern = "___.*", replacement = "")
plot_Basetotale2$word<-toupper(plot_Basetotale2$word)
plot_Basetotale2$word<-trimws(plot_Basetotale2$word)
plot_Basetotale2$plural <- gsub('.{1}$','', plot_Basetotale2$word)
j<-unique(plot_Basetotale2$comcitant)
plot_Basetotale2$dede <- 0
for(i in 1: length(unique(plot_Basetotale2$comcitant))){
k<-which(plot_Basetotale2$comcitant==j[i])
for(m in k){if(any(plot_Basetotale2$plural[m]==plot_Basetotale2$word[k])){
z<-which(plot_Basetotale2$plural[m]==plot_Basetotale2$word[k])
if(k[z]<m){
plot_Basetotale2$dede[k[z]]<-10
plot_Basetotale2$dede[m]<-28}else{
plot_Basetotale2$dede[k[z]]<-28
plot_Basetotale2$dede[m]<-10}
}else{
plot_Basetotale2$dede[m]<-plot_Basetotale2$dede[m]}}
}
plot_Basetotale2<-plot_Basetotale2[plot_Basetotale2$dede!=28,]
plot_Basetotale2<-plot_Basetotale2[,c(1,2)]
j<-unique(plot_Basetotale2$comcitant)
plot_Basetotale2$comp<-0
for(i in 1: length(unique(plot_Basetotale2$comcitant))){
k<-which(plot_Basetotale2$comcitant==j[i])[1:4]
plot_Basetotale2$comp[k]<-1
}
plot_Basetotale2<-plot_Basetotale2[plot_Basetotale2$comp==T,]
for(i in 1: length(unique(plot_Basetotale2$comcitant))){
k<-which(plot_Basetotale2$comcitant==j[i])[1]
plot_Basetotale2$comp[k]<-paste(plot_Basetotale2$word[k],";",plot_Basetotale2$word[k+1],";",plot_Basetotale2$word[k+2],";",plot_Basetotale2$word[k+3])
}
plot_Basetotale2<-plot_Basetotale2[,c(1,3)]
plot_Basetotale2<-plot_Basetotale2[plot_Basetotale2$comp!=1,]
names(plot_Basetotale2)[names(plot_Basetotale2) == "comcitant"] <- "Com_ID"
#################################################################################################################################
#################################################################################################################################
# Fin de l'extraction des mots-clés --------------------------------------------------------------
#################################################################################################################################
#################################################################################################################################
top_nodes <- top_nodes(graph, ordering_column = "degree", top_n = 10, top_n_per_com = 1)
community_labels <- community_labels(graph, community_name_column = "Community_name", community_size_column = "Size_com")
community_labels$identifiant<-str_sub (community_labels$Community_name, 1,2)
for(i in 1:nrow(community_labels)){
j<-which(community_labels$identifiant[i] == plot_Basetotale2)
community_labels$Community_name[i]<- plot_Basetotale2$comp[j]
}
library(ggraph)
library(ggrepel)
library(ggnewscale)
ggraph(graph, "manual", x = x, y = y) +
geom_edge_arc(aes(color = color_edges, width = weight), alpha = 0.4, strength = 0.2, show.legend = FALSE) +
scale_edge_width_continuous(range = c(0.1,2)) +
scale_edge_colour_identity() +
geom_node_point(aes(x=x, y=y, size = degree, fill = color), pch = 21, alpha = 0.9, show.legend = FALSE) +
scale_size_continuous(range = c(0.2,13)) +
scale_fill_identity() +
new_scale("size") +
geom_text_repel(data=top_nodes, aes(x=x, y=y, label = Label), size = 2, fontface="bold", alpha = 1, point.padding=NA, show.legend = FALSE) +
geom_label_repel(data=community_labels, aes(x=x, y=y, label = Community_name, fill = color, size = Size_com), fontface="bold", alpha = 0.9, point.padding=NA, show.legend = FALSE) +
scale_size_continuous(range = c(0.5,5)) +
theme_void()
10-Cocitation 1990-1999
##### On a une colonne d'identifiant unique des articles et une colonne avec les refs biblios
##### recodées, on peut donc soumettre ça au package d'Aurelien
set.seed(12)
setwd("C:/Users/jgrad/Desktop/Network_Quality/Network_Quality")
load("Data_5_nodes.rda")
load("Data_7_finalbase.rda")
library(readr)
library(stringr)
#library(bibliometrix)
library(tidyverse)
library(biblionetwork)
#library(devtools)
#devtools::install_github("agoutsmedt/biblionetwork")
eoeoe2<-eoeoe
simple<-unique(eoeoe2$V2)
simple<-as.data.frame(simple)
#setdiff(desco$Cartel,simple[,1])
desco<-desco[desco$Cartel!=108237,]
comp<-cbind(simple,desco$Cartel)
#desco$sloubi<-str_detect (string = desco$Datepubli, pattern = "2000|2001|2002|2003|2004|2005|2006|2007|2008|2009")
#desco$sloubi<-str_detect (string = desco$Datepubli, pattern = "1970|1971|1972|1973|1974|1975|1976|1977|1978|1979")
#desco$sloubi<-str_detect (string = desco$Datepubli, pattern = "1980|1981|1982|1983|1984|1985|1986|1987|1988|1989")
desco$sloubi<-str_detect (string = desco$Datepubli, pattern = "1990|1991|1992|1993|1994|1995|1996|1997|1998|1999")
#desco$sloubi<-str_detect (string = desco$Datepubli, pattern = "2000|2001|2002|2003|2004|2005|2006|2007|2008|2009")
desco<-desco[desco$sloubi==T,]
Cartel<-desco$Cartel
eoeoe2$toofar<-"AB"
for(i in 1:nrow(eoeoe2)){
if(eoeoe2$V2[i]%in%Cartel){eoeoe2$toofar[i]<-T}else{eoeoe2$toofar[i]<-F}}
eoeoe2<-eoeoe2[eoeoe2$toofar==T,]
Carlito<-unique(eoeoe2$V2)
Carlito<-as.data.frame(Carlito)
Cartel<-as.data.frame(Cartel)
Cabal<-cbind(Carlito,Cartel)
eoeoe2$V2<-as.character(eoeoe2$V2)
eoeoe2$identifiant<-as.character(eoeoe2$identifiant)
desco$Cartel<-as.character(desco$Cartel)
Basefinale<-biblio_coupling(eoeoe2, source = "identifiant", ref = "V2", normalized_weight_only = FALSE, weight_threshold = 2)
#devtools::install_github("agoutsmedt/networkflow")
library(biblionetwork)
library(magrittr)
library(dplyr)
library(tidygraph)
library(networkflow)
#devtools::install_github("agoutsmedt/networkflow")
library(tidygraph)
#devtools::install_github("ParkerICI/vite")
library(vite)
Edges_coupling2<-Basefinale[,-c(4)]
Nodes_coupling2<-unique(eoeoe2$identifiant)
Nodes_coupling2<-as.data.frame(Nodes_coupling2)
Nodes_coupling2$frequence<-0
for(i in 1:nrow(Nodes_coupling2)){
j<-which(Nodes_coupling2$Nodes_coupling2[i] == eoeoe2$identifiant)[1]
Nodes_coupling2$frequence[i]<-eoeoe2$frequence[j]
}
Nodes_coupling2$ids<-Nodes_coupling2$Nodes_coupling2
palette <- c("#1969B3","#01A5D8","#DA3E61","#3CB95F","#E0AF0C","#E25920","#6C7FC9","#DE9493","#CD242E","#6F4288","#B2EEF8","#7FF6FD","#FDB8D6","#8BF9A9","#FEF34A","#FEC57D","#DAEFFB","#FEE3E1","#FBB2A7","#EFD7F2","#5CAADA","#37D4F5","#F5779B","#62E186","#FBDA28","#FB8F4A","#A4B9EA","#FAC2C0","#EB6466","#AD87BC","#0B3074","#00517C","#871B2A","#1A6029","#7C4B05","#8A260E","#2E3679","#793F3F","#840F14","#401C56","#003C65","#741A09","#602A2A","#34134A","#114A1B","#27DDD1","#27DD8D","#4ADD27","#D3DD27","#DDA427","#DF2935","#DD27BC","#BA27DD","#3227DD","#2761DD","#27DDD1")
graph <- tbl_main_component(nodes = Nodes_coupling2, edges = Edges_coupling2, directed = FALSE, node_key = "Cartel", nb_components = 1)
## Varying the resolution of the algorithm results in a different partition and
## different number of communities. A lower resolution means less communities, and conversely.
## The basic resolution of the leiden_workflow() is set by res_1 and equals 1 by default.
## You can vary this parameter, but also try a second resolution with res_2 and a third one
## with res_3
## On peut l'enlever avec un argument par défaut de 1
graph <- leiden_workflow(graph, res_1 = 0.5)
graph <- community_colors(graph, palette, community_column = "Com_ID")
graph <- graph %>%
activate(nodes) %>%
mutate(degree = centrality_degree())
graph <- community_names(graph, ordering_column = "degree", naming = "ids", community_column = "Com_ID")
graph <- graph %>%
activate(nodes) %>%
mutate(size = frequence) # use here what you will use for the size of the nodes in the graph. Force Atlas will take care of avoiding overlapping
######## First.iter initiallement 5000, j'ai baissé pour arriver à le faire tourner
graph <- vite::complete_forceatlas2(graph, first.iter = 10000, overlap.method = "repel", overlap.iter = 1500)## Total number of iterations: 10000
## Total number of iterations: 1500top_nodes <- top_nodes(graph, ordering_column = "degree", top_n = 10, top_n_per_com = 1)
community_labels2 <- community_labels(graph, community_name_column = "Community_name", community_size_column = "Size_com")
library(ggraph)
library(ggrepel)
library(ggnewscale)
graph456<-as.data.frame(graph)
ggraph(graph, "manual", x = x, y = y) +
geom_edge_arc(aes(color = color_edges, width = weight), alpha = 0.4, strength = 0.2, show.legend = FALSE) +
scale_edge_width_continuous(range = c(0.1,2)) +
scale_edge_colour_identity() +
geom_node_point(aes(x=x, y=y, size = degree, fill = color), pch = 21, alpha = 0.9, show.legend = FALSE) +
scale_size_continuous(range = c(0.2,13)) +
scale_fill_identity() +
new_scale("size") +
geom_text_repel(data=top_nodes, aes(x=x, y=y, label = Label), size = 2, fontface="bold", alpha = 1, point.padding=NA, show.legend = FALSE) +
geom_label_repel(data=community_labels2, aes(x=x, y=y, label = Community_name, fill = color, size = Size_com), fontface="bold", alpha = 0.9, point.padding=NA, show.legend = FALSE) +
scale_size_continuous(range = c(0.5,5)) +
theme_void()
graph123<-graph123[,c(1,9)]
graph456<-graph456[,c(1,4)]
eoeoe$comcitant<-0
for(i in 1:nrow(graph123)){
j<-which(eoeoe$V2 == graph123$Id[i])
eoeoe$comcitant[j]<-graph123$Com_ID[i]
}
eoeoe$comcite<-0
for(i in 1:nrow(graph456)){
j<-which(eoeoe$identifiant == graph456$Id[i])
eoeoe$comcite[j]<-graph456$Com_ID[i]
}
eoeoe4<-eoeoe
eoeoe4<-eoeoe4[eoeoe4$comcitant!=0,]
eoeoe4<-eoeoe4[eoeoe4$comcite!=0,]
community_labels2$ID<-gsub("-.*", "",community_labels2$Community_name)
community_labels2$IDil<-gsub(".*-", "",community_labels2$Community_name)
community_labels2$IDcompl<-"AB"
for(i in 1:nrow(community_labels2)){
j<-which(community_labels2$IDil[i] == eoeoe$identifiant)
z<-which(paste(eoeoe$Firstauthor[j],eoeoe$Datepubli[j],eoeoe$Issue[j],eoeoe$Page[j])==eoeoe$identifiant[j])[1]
community_labels2$IDcompl[i] <- eoeoe$V1[j[z]]
}
tableau<-as.data.frame.matrix(prop.table(table(eoeoe4$comcitant,eoeoe4$comcite),margin=1)*100)
library(dplyr)
tableau<-tableau %>% mutate_if(is.numeric, ~round(., 1))
community_labels<-community_labels[order(community_labels$identifiant),]
row.names(tableau) <- community_labels$Community_name
community_labels2<-community_labels2[order(community_labels2$Community_name),]
colnames(tableau)<- community_labels2$ID
for(i in 1:nrow(community_labels2)){
community_labels2$IDcompl[i]<-paste(i,"-",community_labels2$IDcompl[i])
}
Legend<-community_labels2[,c(8)]
Legend<-as.data.frame(Legend)
names(Legend)[names(Legend) == "IDcompl"] <- "Complete reference of the clusters"
library(knitr)
library(kableExtra)
kable(Legend)| Complete reference of the clusters |
|---|
| 1 - Milgrom, P., Roberts, J., Price and advertising signals of product quality (1986) The Journal of Political Economy, 94 (4), pp. 796-821; |
| 2 - Dierickx, I., Cool, K., Asset stock accumulation and the sustainability of competitive advantage (1989) Management Science, 35, pp. 1504-1511; |
| 3 - Saraph, J.V., Benson, P.G., Schroeder, R.G., An instrument for measuring the critical factors of quality management (1989) Decis. Sci., 20 (4), pp. 810-829; |
| 4 - Mussa, M., Rosen, S., Monopoly and product quality (1978) Journal of Economic theory, 18, pp. 301-317; |
| 5 - Katz, M.L., Shapiro, C., Technology adoption in the presence of network externalities (1986) Journal of Political Economy, 94, pp. 822-841; |
| 6 - Parasuraman, A., Zeithaml, V.A., Berry, L.L., A conceptual model of service quality and its implications for future research (1985) Journal of Marketing, 49, pp. 41-50; |
| 7 - Hart, S.L., A natural-resource-based view of the firm (1995) Academy of Management Review, 20 (4), pp. 986-1014; |
| 8 - Allen, R., Gertler, P., Regulation and the provision of quality to heterogeneous consumers: The case of prospective pricing of medical services (1991) Journal of Regulatory Economics, 3, pp. 361-375; |
| 9 - Dunne, T., Roberts, M.R., Samuelson, L., Patterns of firm entry and exit in U.S. manufacturing industries (1988) RAND Journal of Economics, 19 (4), pp. 495-515; |
| 10 - Hansen, L., Large sample properties of generalized method of moments estimators (1982) Econometrica, 50, pp. 1029-1054; |
| 11 - Linder, S., (1961) An Essay on Trade and Transformation, , Uppsala, Almqvist & Wiksells; |
| 12 - Rayner, P., Porter, L.J., BS5759/ISO 9000 - The experience of small and medium sized firms (1991) International Journal of Quality & Reliability Management, 8, pp. 16-28; |
| 13 - Bentler, P.M., Bonett, D.G., Significant tests and goodness-of-fit in the analysis of covariance structures (1980) Psychological Bulleting, 88, pp. 588-606; |
| 14 - Glaser, B.J., Strauss, A.L., (1967) The Discovery of Grounded Theory: Strategies for Qualitative Research, , Aldine Transaction, Piscataway, NJ; |
| 15 - Frank, R., The demand for unobservable and other non-positional goods (1985) American Economic Review, 75 (1), pp. 101-116; |
| 16 - Caswell, J.A., Johnson, G.V., Firm Strategic Response to Food Safety and Nutrition Regulation (1991) Economics of Food Safety, , J.A. Caswell (Ed) Elsevier Science Publishing Company New York; |
| 17 - Barclay, M.J., Litzenberger, R.H., Announcement effects of new equity issues and use of intraday price data (1988) Journal of Financial Economics, 21, pp. 71-99; |
| 18 - Nelson, R.R., (1993) National Innovation Systems: A Comparative Analysis, , Oxford University Press: New York; |
| 19 - Joskow, P., Vertical Integration and Long-Term Contracts: The Case of Coal-Burning Electric Generation Plants (1985) Journal of Law, Economics, & Organization, 1, pp. 33-80; |
11- Connect the communities 1990-1999
########################################################################################
##############################################################################################################
# Connecter les communautés -----------------------------
##############################################################################################################
##############################################################################################################
tableau %>%
kbl() %>%
kable_styling()| 01 | 02 | 03 | 04 | 05 | 06 | 07 | 08 | 09 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ADVERTISING ; REPUTATION ; SELLERS ; INTERNET | 86.8 | 2.2 | 1.0 | 3.9 | 1.3 | 1.7 | 0.1 | 0.5 | 0.2 | 0.1 | 0.5 | 0.0 | 0.0 | 0.0 | 1.3 | 0.2 | 0.0 | 0.0 | 0.1 |
| TQM ; MANAGEMENT ; ALLIANCES ; ISO | 2.2 | 38.6 | 43.1 | 0.8 | 0.7 | 1.9 | 8.2 | 0.1 | 0.0 | 0.2 | 0.1 | 0.4 | 1.4 | 0.9 | 0.2 | 0.0 | 0.3 | 0.4 | 0.4 |
| MANUFACTURER ; CHANNEL ; RETAILER ; DIFFERENTIATION | 12.5 | 1.0 | 0.2 | 78.2 | 3.1 | 0.0 | 0.2 | 1.2 | 1.9 | 0.3 | 1.4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| NETWORK ; SOFTWARE ; STANDARDS ; HARDWARE | 4.8 | 17.4 | 0.8 | 18.2 | 57.7 | 0.0 | 0.6 | 0.1 | 0.0 | 0.1 | 0.1 | 0.0 | 0.0 | 0.2 | 0.1 | 0.0 | 0.0 | 0.1 | 0.0 |
| PATIENTS ; HOSPITAL ; PROVIDER ; CARDIAC | 12.7 | 2.6 | 0.9 | 10.5 | 1.3 | 0.4 | 0.4 | 43.0 | 0.9 | 27.2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| SATISFACTION ; SERVICE ; SERVQUAL ; CUSTOMER | 0.4 | 0.6 | 5.9 | 0.2 | 0.0 | 91.1 | 0.2 | 0.2 | 0.0 | 0.0 | 0.0 | 0.2 | 0.0 | 1.2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| EXPORTING ; EXPORTERS ; COUPONING ; PARAMETERS | 28.7 | 5.8 | 1.2 | 9.9 | 0.6 | 2.3 | 0.6 | 1.8 | 39.8 | 8.8 | 0.6 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| TRADE ; LICENSING ; SPECTRUM ; NORTHERN | 15.5 | 5.2 | 1.0 | 9.3 | 7.2 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 59.8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 9000 ; ISO ; BUSINESSES ; SMES | 0.0 | 0.0 | 7.2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 92.8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| FUNDS ; EARNINGS ; MUTUAL ; BOND | 28.6 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 71.4 | 0.0 | 0.0 |
| SAFETY ; FOOD ; REGULATION ; PROTECTION | 19.0 | 4.8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 4.8 | 0.0 | 71.4 | 0.0 | 0.0 | 0.0 |
| BOX ; FILMS ; OFFICE ; MOTION | 66.7 | 0.0 | 0.0 | 33.3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| EXPERIMENTATION ; PAYOFF ; OLIGOPOLISTIC ; DISPERSION | 92.6 | 0.0 | 0.0 | 3.7 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 3.7 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |